People	Publication	Research	Teaching	Service	Awards

 Publication

 

Summary: (click each one with hyperlink)

 

     24 Representative Publications

     3 Pending Patents

1 PhD Dissertation

4 Book Chapters

227 Published or Accepted Referred Journal Papers

51 Conference Proceeding Papers

6 Formal Reports

38 Submitted Journal Papers

6 Keynote/plenary speeches

187 Invited Presentations

 

24 Representative publications: (* denotes corresponding author)

1. Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu, Bayesian Optimization with Gaussian-Process based Machine Learning for Improvement of Geometric Accuracy in Projection Multi-photon 3D Printing, Light: Science & Applications (Nature, Impact Factor 20.6), 14, 56, 2025.

https://doi.org/10.1038/s41377-024-01707-8

 

2.    Zhang, Y., Zhang, S., Wu, H., Wang, J., Lin, G.*, Zhang, A.P. Miniature computational spectrometer with a plasmonic nanoparticles-in-cavity microfilter array. Nat Commun 15, 3807 (2024).

https://www.nature.com/articles/s41467-024-47487-y

 

3.    Ziqi Guo, Zherui Han, Dudong Feng, Guang Lin*, Xiulin Ruan, Sampling-accelerated prediction of phonon scattering rates for converged thermal conductivity and radiative properties, Npj Computational Materials, 10, 31, 2024.

https://doi.org/10.1038/s41524-024-01215-8

 

4.    Ziqi Guo, Roy Chowdhury Prabudhya1, Zherui Han, Yixuan Sun, Dudong Feng, Guang Lin*, and Xiulin Ruan, Fast and Accurate Machine Learning of Phonon Scattering Rates and Lattice Thermal Conductivity, Nature npj Computational Material 9, 95, 2023.

https://doi.org/10.1038/s41524-023-01020-9

 

5.    Haoyang Zheng, Jeffrey Petrella, P. Murali Doraiswamy, Guang Lin*, Wenrui Hao, Data-driven causal model discovery and personalized prediction in Alzheimer’s disease, Nature NPJ Digital Medicine, 5, 137, 2022.

https://www.nature.com/articles/s41746-022-00632-7

 

6.    Yixuan Sun, Surya Mitra Ayalasomayajula, Abhas Deva, Guang Lin*, R. Edwin Garcia, Artificial Intelligence Inferred Microstructural Properties from Voltage-Capacity Curves, Scientific Reports, 12, 13421, 2022.

https://doi.org/10.1038/s41598-022-16942-5

 

7.    Ehsan Kharazmi, Min Cai, Xiaoning Zheng, Guang Lin, George Karniadakis, Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks, Nature Computational Science, 1-10, 2021.

https://doi.org/10.1038/s43588-021-00158-0

 

8.    Guang Lin, Chau-Hsing Su and George E. Karniadakis, The stochastic piston problem, Proceedings of the National Academy of Sciences of the United States of America, 101(45):15840-15845, 2004.

https://doi.org/10.1073/pnas.0405889101

 

9.    Guang Lin, Chau-Hsing Su and George E. Karniadakis, Random Roughness Enhances Lift in Supersonic Flow, Physical Review Letters, 99:104501, 2007.

https://doi.org/10.1103/PhysRevLett.99.104501

 

10. Sheng Zhang, Guang Lin*, Samy Tindel, Two-dimensional signature of images and texture classification, Proceeding of the Royal Society of London. Series A, mathematical, physical and engineering sciences, A.478:20220346, 2022.

https://royalsocietypublishing.org/doi/abs/10.1098/rspa.2022.0346

 

11. Sheng Zhang, Guang Lin*, Robust data-driven discovery of governing physical laws with error bars, Proceedings of the Royal Society of London. Series A, mathematical, physical and engineering sciences, A 474: 20180305, 2018.  https://doi.org/10.1098/rspa.2018.0305

 

12. Yifan Du, Guang Lin*，Turbulence Generation from a stochastic wavelet model, Proceeding of the Royal Society of London. Series A, mathematical, physical and engineering sciences, 474(2217):20180093, 2018.

https://doi.org/10.1098/rspa.2018.0093

 

13. Bledar A. Konomi, Georgios Karagiannis, Kevin Lai, Guang Lin*, Bayesian treed Calibration: an application to Carbon capture with AX sorbent, Journal of American Statistical Association, 112(517): 37-53, 2017.

https://doi.org/10.1080/01621459.2016.1190279

 

14. F. Liang, Y. Cheng, and G Lin*, Simulated Stochastic Approximation Annealing for Global Optimization with a Square-Root Cooling Schedule, Journal of the American Statistical Association, 109(506): 847-863, 2014.

https://www.tandfonline.com/doi/abs/10.1080/01621459.2013.872993

 

15. Wei Deng, Xiao Zhang, Faming Liang, Guang Lin*, An adaptive empirical Bayesian method for sparse deep learning, 2019 Conference on Neural Information Processing Systems (NeurIPS), accepted, Dec. 8 – Dec. 14, 2019, Vancouver, Canada. (Tier 1 AI conference)

https://pmc.ncbi.nlm.nih.gov/articles/PMC7687285/

 

16. Wei Deng, Faming Liang, Guang Lin*, A contour stochastic gradient Langevin dynamics algorithm for simulations of multi-modal distributions, 2020 Conference on Neural Information Processing Systems (NeurIPS), Dec. 5 – Dec. 12, 2020, virtual meeting. (Tier 1 AI conference)

https://pmc.ncbi.nlm.nih.gov/articles/PMC8457681/

 

17. Wei Deng, Qi Feng, Liaoyao Gao, F. Liang, G. Lin*, Non-convex learning via replica exchange stochastic gradient MCMC, 2020 International Conference on Machine Learning (ICML), accepted, Jul 12 - 18, 2020, Virtual Meeting. (Tier 1 AI conference)

https://proceedings.mlr.press/v119/deng20b.html

 

18. Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin*, Faming Liang, Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction, The Ninth International Conference on Learning Representations (ICLR), May 4^th-7^th, 2021, accepted (virtual meeting). (Tier 1 AI conference)

https://openreview.net/forum?id=iOnhIy-a-0n

 

19. Wei Deng, Siqi Liang, Botao Hao, Guang Lin*, Faming Liang, Interacting Contour Stochastic Gradient Langevin Dynamics, The Tenth International Conference on Learning Representations (ICLR) 2022, Virtual Meeting. (Tier 1 AI conference), Apr 25^th – 29^th, accepted.

https://openreview.net/forum?id=IK9ap6nxXr2

 

20. Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin*, Non-reversible Parallel Tempering for Uncertainty Approximation in Deep Learning, Thirty-seventh AAAI Conference on Artificial Intelligence, accepted, 2023. (Oral accepted talk)

https://ojs.aaai.org/index.php/AAAI/article/view/25893

 

21. Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin*, Accelerating approximate Thompson sampling with underdamped Langevin Monte Carlo, The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), May 2nd – 4th, 2024, Valencia, Spain, PMLR 238:2611-2619, 2024.

https://proceedings.mlr.press/v238/zheng24b/zheng24b.pdf

 

22. Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin*, Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics, accepted, ICML 2024.

https://openreview.net/forum?id=fwyuupgAQ5

 

23. Jinwon Sohn, Guang Lin*, Qifan Song, Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes, The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), May 2nd – 4th, 2024, Valencia, Spain, PMLR 238:1594-1602, 2024.

https://proceedings.mlr.press/v238/sohn24a/sohn24a.pdf

 

24. Wei Deng, Yian Ma, Zhao Song, Qian Zhang, Guang Lin*, On Convergence of Federated Averaging Langevin Dynamics, 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024), Oral presentation, 2024.

https://openreview.net/forum?id=EmQGdBsOPx

 

3 Pending Patents

1. Yixuan Sun, Abhas Deva, Ramiro Edwin Garcia, Guang Lin, Machine Learning-Based Method to Determine Microstructural Performance Properties of Porous Rechargeable Batteries from Voltage-Time Plots, Disclosure Number: D2020-0324.

2. Frederick Damen, David Newton, Guang Lin, Craig Goergen, SYSTEM AND METHODS FOR MACHINE LEARNING DRIVEN CONTOURING CARDIAC ULTRASOUND DATA, Patent Atty Dkt. No. 69227-02.

3. Jeffrey Petrella, Murali Doraiswamy, Guang Lin, Wenrui Hao, Math Model for Alzheimer’s Disease, 2022, Invention Disclosure 2022-5453 (Filed from Penn State University)

 

 

1 Doctoral Dissertation:

 

Parallel High-Order Methods for Deterministic and Stochastic CFD and MHD Problems, Advisor: George Em Karniadakis, Brown University, 2007.

 

4 Book Chapters:

 

4. Emily Kang, Alex Konomi, Guang Lin, Enhancing Gaussian Process for Surrogate Modeling: A Review of Dimension Reduction Techniques for Input Variables, 2024.

 

3. Clarence Mybee, Guang Lin, Wei Zakharov, Chao Cai, Jason FitzSimmons, and Yixuan Sun, “Building Undergraduate Data Literacy through Faculty Development” chapter in Teaching Critical Thinking with Numbers: Data Literacy and the Framework for Information Literacy for Higher Education, published by ALA Editions, 2020.

 

2. G. Lin, G. Karniadakis, Stochastic Systems Chapter in Encyclopedia of Applied and Computational Mathematics, ed. Bjorn Engquist, Springer, New York, 2015.

 

1.G. Lin, Big Data Application in Power Grid Systems chapter in CRC Handbook on Big Data, CRC Press, Taylor & Francis Group, 2016

 

 

227 Published or Accepted Journal Papers and 38 Submitted Journal Papers:

 

(1) 227 Published or Accepted Journal Papers (* denotes corresponding author)

 

 

Journal Paper from 2025

 

[P227] Jason E. Johnson, Ishat Raihan Jamil, Liang Pan, Guang Lin, Xianfan Xu, Bayesian Optimization with Gaussian-Process based Machine Learning for Improvement of Geometric Accuracy in Projection Multi-photon 3D Printing, Light: Science & Applications (Nature, Impact Factor 20.6), 14, 56, 2025.

https://doi.org/10.1038/s41377-024-01707-8

 

[P226] Christian Moya, Amir Mollaali, Zecheng Zhang, Lu Lu, Guang Lin*, Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks, Physica D, Nonlinear Phenomena, 471: 134418, 2025.

https://www.sciencedirect.com/science/article/pii/S0167278924003683

 

Journal Paper from 2024

[P225] Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin*, Hayden Schaeffer, D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators, Computer Methods in Applied Mechanics and Engineering, 428: 117084, 2024.

https://epubs.siam.org/doi/abs/10.1137/23M160342X

 

[P224] Shiheng Zhang, Jiahao Zhang, Jie Shen, Guang Lin*, An element-wise RSAV algorithm for unconstrained optimization problems, SIAM Journal on Scientific Computing, in press, 2024.

https://arxiv.org/abs/2309.04013

 

[P223] Siyang Nie,Yan Xiang, Liang Wu, Guang Lin*, Qingda Liu, Shengqi Chu, Xun Wang, Active Learning Guided Discovery of High Entropy Oxides Featuring Electron Delocalization Properties and High H2-production, Journal of the American Chemical Society, 146, 43, 29325–29334, 2024.

 https://pubs.acs.org/doi/10.1021/jacs.4c06272

 

[P222] Adil Wazeer, Tanner McElroy, Benjamin Thomas Stegman, Anyu Shang, Yifan Zhang, Vaibhav Singh, Huan Li, Zhongxia Shang, Haiyan Wang, Yexiang Xue, Guang Lin, Timothy Graening, Guannan Zhang, Xiao-Ying Yu, Xinghang Zhang, A Review of the Impact of Neutron Irradiation Damage in Tungsten and its Alloys, Metals, Metals, 14, 1374, 2024.

https://doi.org/10.3390/met14121374

 

[P221] Yixuan Sun, Imad Hanhan, Michael D. Sangid, Guang Lin*, Predicting Mechanical Properties from Microstructure Images in Fiber-reinforced Polymers using Convolutional Neural Networks, Journal of Composite Science, 8(10), 387, 2024. (Issue Cover page).

https://doi.org/10.3390/jcs8100387

 

[P220] Jiajun Liang, Qian Zhang, Wei Deng, Guang Lin*, Qifan Song, Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory, Journal of Computational and Graphical Statistics, 1-10, 2024.

https://www.tandfonline.com/doi/full/10.1080/10618600.2024.2380051

 

[P219] Conner C. Earl, Craig J. Goergen, Alexa M. Jauregui, Kan N. Hor, Larry W. Markham, Jonathan H. Soslow, Guang Lin*, Regional 4D Cardiac Magnetic Resonance Strain Predicts Cardiomyopathy Progression in Duchenne Muscular Dystrophy, Journal of Cardiovascular Magnetic Resonance, 26, 1, 100194, 2024.

https://doi.org/10.1016/j.jocmr.2024.100194

 

[P218] Na Ou, Zecheng Zhang, Guang Lin*, A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method with Multi-variance Strategy for Bayesian inverse problems, Journal of Computational Physics, 510, 113067, 2024.

 https://www.sciencedirect.com/science/article/abs/pii/S0021999124003164

 

[P217] Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin* and Hayden Schaeffer. D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators, Computer Methods in Applied Mechanics and Engineering, 428, 117084, 2024.

https://www.sciencedirect.com/science/article/abs/pii/S0045782524003402

 

[P216] Zhang, Y., Zhang, S., Wu, H., Wang, J., Lin, G.,* Zhang, A.P. Miniature computational spectrometer with a plasmonic nanoparticles-in-cavity microfilter array. Nat Commun 15, 3807 (2024).

https://www.nature.com/articles/s41467-024-47487-y

 

[P215] Yikai Liu, Tushar K. Ghosh, Guang Lin*, Ming Chen, Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model, J. Phys. Chem. Lett. 15, 14, 3938-3945, 2024.

https://pubs.acs.org/doi/10.1021/acs.jpclett.3c03515

 

[P214] Guang Lin*, Na Ou, Zecheng Zhang, Zhidong Zhang, Restoring the discontinuous heat equation source using sparse boundary data and dynamic sensors, Inverse Problems, 40, 045014, 2024.

https://iopscience.iop.org/article/10.1088/1361-6420/ad2904

 

[P213] Yuheng Wang, Guang Lin, Shengfeng Yang, Integrating Uncertainty into Deep Learning Models for Enhanced Prediction of Nanocomposite Materials' Mechanical Properties, APL Machine Learning, 2, 016112, 2024.

https://pubs.aip.org/aip/aml/article/2/1/016112/3266835/Integrating-uncertainty-into-deep-learning-models

 

[P212] Ziqi Guo, Zherui Han, Dudong Feng, Guang Lin*, Xiulin Ruan, Sampling-accelerated prediction of phonon scattering rates for converged thermal conductivity and radiative properties, Npj Computational Materials, 10, 31, 2024.

https://doi.org/10.1038/s41524-024-01215-8

 

[P211] Zecheng Zhang, Christian Moya, Wing Tat Leung, Guang Lin*, Hayden Schaeffer, Bayesian deep operator learning for homogenized to fine-scale maps for multiscale PDE, SIAM Multiscale Modeling and Simulation, 22:3, 10.1137/23M160342X, 2024.

https://doi.org/10.1137/23M160342X

[P210] Haoyang Zheng, Yao Huang, Ziyang Huang, Wenrui Hao, Guang Lin*, HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions, Journal of Computational Physics, 500, 112751, 2024.

https://www.sciencedirect.com/science/article/abs/pii/S0021999123008471

 

[P209] Izzet Sahin, Christian Moya, Amirhossein Mollaali, Guang Lin*, Guillermo Paniagua, Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles, International Journal of Heat and Mass Transfer, 219, 124813, 2024.

https://www.sciencedirect.com/science/article/abs/pii/S0017931023009584

 

[P208] Jiahao Zhang, Shiheng Zhang, Jie Shen, Guang Lin*, Energy-Dissipative Evolutionary Deep Operator Neural Networks, Journal of Computational Physics, 498, 112638, 2024.

https://www.sciencedirect.com/science/article/abs/pii/S0021999123007337

 

Journal Paper from 2023

 

[P207] Yan Xiang, Yu-Hang Tang, Zheng Gong, Hongyi Liu, Liang Wu, Guang Lin*, Huai Sun, Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules, Journal of Chemical Information and Modeling, 63,21, 6515-6524, 2023.

https://pubs.acs.org/doi/10.1021/acs.jcim.3c01430

 

[P206] Amirhossein Mollaali, Izzet Sahin, Iqrar Raza, Christian Moya, Guillermo Paniagua, Guang Lin*, A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients, Fluids, 8(12), 323, 2023.

https://doi.org/10.3390/fluids8120323.

 

[P205] Guang Lin*, Christian Moya, Zecheng Zhang, Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks, Engineering Applications of Artificial Intelligence, 125, 106689, 2023.

https://www.sciencedirect.com/science/article/abs/pii/S0952197623008734

 

[P204] Yuepeng Wang, Jie Li, Wenju Zhao, I.M. Navon, Guang Lin*, Accelerating Inverse Inference of Ensemble Kalman Filter via Reduced-order Model Trained Using Adaptive Sparse Observations, 496, 112600, 2023.

https://www.sciencedirect.com/science/article/abs/pii/S0021999123006952

 

[P203] Xiaohui Li, Peipei Zhu, Yen-Ju Chen, Lei Huang, Diwen Wang, David T. Newton, Chuan-Chih Hsu, Guang Lin, W. Andy Tao, Christopher J. Staiger, Chunhua Zhang, The EXO70 inhibitor Endosidin2 alters plasma membrane protein composition in Arabidopsis roots, Front Plant Sci.,14, 1171957, 2023.

https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1171957/full

 

[P202] Yixuan Sun, Christian Moya, Guang Lin*, Meng Yue, DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-shot Transfer the Dynamic Response of Networked Systems, IEEE Systems Journal, 17(3): 4360 - 4370, 2023.

https://ieeexplore.ieee.org/document/10210333

 

[P201] Yan Xiang, Yu-Hang Tang, Guang Lin, Daniel Reker, Interpretable molecular property predictions using marginalized graph kernel, Journal of Chemical Information and Modeling, 63, 15, 4633-4640, 2023.

https://pubs.acs.org/doi/abs/10.1021/acs.jcim.3c00396

 

[P200] Christian Moya, Guang Lin*, Bayesian, Multi-fidelity Operator Learning for Complex Engineering Systems – A Position Paper, ASME Journal of Computing and Information Science in Engineering special issue on Challenges and Opportunities in Computing Research to Enable Next Generation Engineering Applications, 23(6): 060814, 2023.

https://doi.org/10.1115/1.4062635

 

[P199] Guangshuai Han, Yixuan Sun, Yining Feng, Guang Lin*, Na Lu, Artificial intelligence guided thermoelectric materials design and discovery, Advanced Electronic Materials, 9(8): 2300042, 2023. (cover page)

https://doi.org/10.1002/aelm.202300042

 

[P198] Binghang Lu, Christian Moya, Guang Lin*, NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training, Algorithms, 16(4), 194, 2023.

https://doi.org/10.3390/a16040194

 

[P197] Hugo Esquivel, Guang Lin, Amending Section R8.4.4.2.3 of ACI 318-19 and other sources: a brief discussion on the Jc method for slab-column connections, Civil Engineering Journal, 9 (11), 2847, 2023.

https://www.civilejournal.org/index.php/cej/article/view/4446

 

[P196] Susanna Lange, Wei Deng, Qiang Ye, Guang Lin*, Batch Normalization Preconditioning for Stochastic Gradient Langevin Dynamics, Journal of Machine Learning, 2(1):65-82, 2023.

https://doi.org/10.4208/jml.220726a

 

[P195] Ziqi Guo, Roy Chowdhury Prabudhya1, Zherui Han, Yixuan Sun, Dudong Feng, Guang Lin*, and Xiulin Ruan, Fast and Accurate Machine Learning of Phonon Scattering Rates and Lattice Thermal Conductivity, Nature npj Computational Material 9, 95, 2023.

https://doi.org/10.1038/s41524-023-01020-9

 

[P194] Xinchao Liu, Xinchao Liu, Xiao Liu, Tulin Kaman, Guang Lin, Physics-Informed Statistical Learning for Nonlinear Structural Dynamics of Aircraft-UAV Collisions, Technometrics, 65(4):564, 2023,

https://doi.org/10.1080/00401706.2023.2203175

 

[P193] Christian Moya, Shiqi Zhang, Meng Yue, Guang Lin*, DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories, Neurocomputing, 535:166-182, 2023.

https://doi.org/10.1016/j.neucom.2023.03.015

 

[P192] Moonseop Kim, Guang Lin*, Peri-Net-Pro: The neural processes with quantified uncertainty for analysis of crack patterns, Applied Mathematics and Mechanics, 44(1), 2023.

https://doi.org/10.1007/s10483-023-2991-9

 

[P191] Christian Moya, Guang Lin*, DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential Algebraic Equations with Application to Power Networks, Neural Computing and Applications, 35, 3789-3804, 2023.

https://link.springer.com/article/10.1007/s00521-022-07886-y

 

 

[P190] Guang Lin*, Christian Moya, Zecheng Zhang, B-DeepONet: An Enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD, Journal of Computational Physics, 473:111713, 2023.

https://doi.org/10.1016/j.jcp.2022.111713

 

Journal Paper from 2022

 

[P189] Sandra De Iaco, Dionissios T. Hristopulos, Guang Lin, Special Issue: Geostatics and Machine Learning, Mathematical Geosciences, 54, 459-465, 2022.

https://doi.org/10.1007/s11004-022-09998-6

 

[P188] Shirley Rietdyk, Satyajit Ambike, Steve Amireault, Jeffrey M. Haddad, Guang Lin, David Newton, Libby A. Richards, Co-occurrences of fall-related factors in adults 60 to 85 years old: A cluster analysis using data from the United States National Health and Nutrition Examination Survey. Plos One, 17(11): e0277406, 2022.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277406

 

[P187] Guang Lin*, Zecheng Zhang, Zhidong Zhang, Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements, Inverse Problems, 38:125007, 2022.

https://iopscience.iop.org/article/10.1088/1361-6420/ac99f9/meta

 

[P186] Ziyang Huang, Guang Lin*, Arezoo M. Ardekani, Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows, Journal of Computational Physics, 471, 111619, 2022.

https://doi.org/10.1016/j.jcp.2022.111619

 

[P185] Haoyang Zheng, Ziyang Huang, Guang Lin*, PCNN: A physics-constrained neural network for multiphase flows, Physics of Fluids, 34, 102102, 2022.

https://doi.org/10.1063/5.0111275

 

[P184] Christian Moya, Guang Lin*, Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks, Algorithms, 15(9), 325, 2022.

https://doi.org/10.3390/a15090325

 

[P183] Sheng Zhang, Guang Lin*, Samy Tindel, Two-dimensional signature of images and texture classification, Proceeding of the Royal Society of London. Series A, mathematical, physical and engineering sciences, A.478:20220346, 2022.

https://royalsocietypublishing.org/doi/abs/10.1098/rspa.2022.0346

 

[P182] Liyao Gao, Wei Zhu, Guang Lin*, Deformation Robust Roto-Scale-Translation Equivariant CNNs, Transactions on Machine Learning Research, ID: yVkpxs77cD, 2022.

https://openreview.net/pdf?id=yVkpxs77cD

 

[P181] Wing Tat Leung, Guang Lin*, Zecheng Zhang, NH-PINN: Neural homogenization based Physics-informed Neural Network for Multiscale Problems, Journal of Computational Physics, 470, 111539, 2022.

https://www.sciencedirect.com/science/article/abs/pii/S0021999122006015

 

[P180] Hugo Esquivel, Arun Prakash, Guang Lin*, Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems, Journal of Computational Physics, 111425, 2022.

https://doi.org/10.1016/j.jcp.2022.111425

 

[P179] Wei Deng, Guang Lin, Faming Liang, An Adaptively Weighted Stochastic Gradient MCMC Algorithm for Global Optimization in Deep Learning, Statistics and Computing, 32, 58, 2022.

https://doi.org/10.1007/s11222-022-10120-3

 

[P178] Haoyang Zheng, Jeffrey Petrella, P. Murali Doraiswamy, Guang Lin*, Wenrui Hao, Data-driven causal model discovery and personalized prediction in Alzheimer’s disease, Nature NPJ Digital Medicine, 5, 137, 2022.

https://www.nature.com/articles/s41746-022-00632-7

 

[P177] Shruthi Suresh, David T. Newton, Thomas H. Everett IV, Guang Lin, Bradley S. Duerstock, Feature selection techniques for machine learning model to detect autonomic dysreflexia, Frontiers in Neuroinformatics, 16:901428, 2022.

https://www.frontiersin.org/articles/10.3389/fninf.2022.901428/full

 

[P176] Francisco Lozano, Iman Rahbari, Guang Lin, Guillermo Paniagua, Stochastic boundary condition effects on supersonic leading edge blowing, Computers and Fluids, 243:105513, 2022.

https://doi.org/10.1016/j.compfluid.2022.105513

 

[P175] Yixuan Sun, Surya Mitra Ayalasomayajula, Abhas Deva, Guang Lin*, R. Edwin Garcia, Artificial Intelligence Inferred Microstructural Properties from Voltage-Capacity Curves, Scientific Reports, 12, 13421, 2022.

https://doi.org/10.1038/s41598-022-16942-5

 

[P174] Georgios Karagiannis, Zhangshuan Hou, Maoyi Huang, Guang Lin*, Inverse modeling of hydrologic parameters in CLM4 via generalized polynomial chaos in the Bayesian framework, Computation, 10(5), 72, 2022.

https://doi.org/10.3390/computation10050072

 

[P173] Liyao Gao, Yifan Du, Hongshan Li, Guang Lin*, RotEqNet: Rotation-Eqvariant network for fluid systems with symmetric high-order tensors, Journal of Computational Physics, 461: 111205, 2022.

https://doi.org/10.1016/j.jcp.2022.111205

 

[P172] Dongwu Wang, Bin Zheng, Long Chen, Guang Lin*, Jinchao Xu, Block Triangular Preconditioning for Stochastic Galerkin Method, Journal of Computational and Applied Mathematics, 412: 114298, 2022.

https://doi.org/10.1016/j.cam.2022.114298

 

[P171]       Maya Lapp, Guang Lin, Alexander Komin, Leah Andrews, Mei Knudson, Lauren Mossman, Giorgio Raimondi, Julia Arciero, Modeling the potential of Treg-based therapies for transplant rejection: effect of dose, timing, and accumulation site, Transplant International, 35:10297, 2022.

https://www.frontierspartnerships.org/articles/10.3389/ti.2022.10297/full

 

[P170] Tehuan Chen, Zhigang Ren, Guang Lin*, Chao Xu, Learning PDE-based Approximate Optimal Control for an MHD System with Uncertainty Quantification, IEEE Transactions on Systems, Man, and Cybernetics, 52(11):7185-7192, 2022.

https://doi.org/10.1109/TSMC.2022.3152505

 

[P169] Guang Lin*, Yating Wang, Zecheng Zhang, Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN, Journal of Computational Physics, 460, 111173, 2022.

 https://doi.org/10.1016/j.jcp.2022.111173.

 

[P168] Zecheng Zhang, Yalchin Efendiev, Wing Tat Leung, Guang Lin*, Efficient hybrid explicit-implicit learning for multiscale problems, Journal of Computational Physics, 467, 111326, 2022.

https://doi.org/10.1016/j.jcp.2022.111326

 

[P167] Ziyang Huang, Guang Lin*, Arezoo M. Ardekani, A consistent and conservative Phase-Field method for multiphase flows, Journal of Computational and Applied Mathematics, 408: 114116, 2022.

https://www.sciencedirect.com/science/article/abs/pii/S0377042722000218

 

[P166] Suman Chakraborty, Yixuan Sun, Guang Lin*, Li Qiao, Vapor-liquid equilibrium predictions of n-alkane/nitrogen mixtures using neural networks, Journal of Computational and Applied Mathematics, 408: 114059, 2022.

https://www.sciencedirect.com/science/article/abs/pii/S0377042721006208

 

[P165] Yao Huang, Wenrui Hao, Guang Lin*, HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear differential equations, Computers and Mathematics with Applications, 121: 62-73, 2022.

https://www.sciencedirect.com/science/article/abs/pii/S0898122122002851

 

[P164] Sheng Zhang, Xiu Yang, Samy Tindel, Guang Lin*, Augmented Gaussian random field: Theory and computation, Discrete and Continuous Dynamical Systems - S, 15(4): 931-957, 2022.

https://www.aimsciences.org/article/doi/10.3934/dcdss.2021098

 

[P163] Ziyang Huang, Guang Lin*, Arezoo M. Ardekani, A consistent and conservative Phase-Field model for thermo-gas-liquid-solid flows including liquid-solid phase change, Journal of Computational Physics, 449, 110795, 2022.

https://doi.org/10.1016/j.jcp.2021.110795

 

[P162] Yating Wang, Wing Tat Leung, Guang Lin*, AMS-NET: Adaptive multiscale sparse neural basis expansion for interpretable deep learning, SIAM Multiscale Modeling Simulation, 20(2), 10.1137/21M1405289, 2022.

https://doi.org/10.1137/21M1405289

 

Journal Paper from 2021

 

[P161] Yan Xiang, Yu-Hang Tang, Guang Lin*, Huai Sun, A Comparative Study of Marginalized Graph Kernel and Message Passing Neural Network, Journal of Chemical Information and Modeling, 61, 11, 5414-5424, 2021.

https://doi.org/10.1021/acs.jcim.1c01118

 

[P160] Hengnian Yan, Chenyu Hao, Jiangjiang Zhang, Walter Illman, Guang Lin, Lingzao Zeng,  Accelerating Groundwater Data Assimilation with a Gradient-Free Active Subspace Method, Water Resources Research, 57 (12), 2021, https://doi.org/10.1029/2021WR029610

 

[P159] Ehsan Kharazmi, Min Cai, Xiaoning Zheng, Guang Lin, George Karniadakis, Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks, Nature Computational Science, 1-10, 2021.

https://doi.org/10.1038/s43588-021-00158-0

 

[P158] Sheng Zhang, Joan Ponce, Zhen Zhang, Guang Lin*, George Karniadakis, An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City, PLOS Computational Biology, 17(9): e1009334, 2021.

https://doi.org/10.1371/journal.pcbi.1009334

 

[P157] Xiaolong Hu, Liangsheng Shi, Guang Lin, Lin Lin, Comparison of physical-based, data-driven and hybrid modeling approaches for evapotranspiration prediction, Journal of Hydrology, 601, 126592, 2021.

https://www.sciencedirect.com/science/article/abs/pii/S0022169421006405

 

[P156] Hugo Raul Esquivel, Arun Prakash, Guang Lin*, Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems, Journal of Computational and Applied Mathematics, 398:113674, 2021.

https://doi.org/10.1016/j.cam.2021.113674

 

[P155] Ranis N. Ibragimov, Lauren D. Mongrain, Benjamin Stimmel, Olga Trozkaya, Guang Lin*, Sheng Zhang, Vesselin Vatchev, Daniel Stankiewicz, Visualization of Exact Invariant Solutions Associated with Atmospheric Waves in a Thin Circular Layer, Journal of Applied Mathematics and Physics, 9(5), 901-919, 2021.

https://www.scirp.org/journal/paperinformation?paperid=109163

 

[P154] Jun Man, Guang Lin*, Yijun Yao, Lingzao Zeng, A Generalized Multi-Fidelity Simulation Method Using Sparse Polynomial Chaos Expansion, Journal of Computational and Applied Mathematics, 397:113613, 2021.

https://doi.org/10.1016/j.cam.2021.113613

 

[P153] Yan Xiang, Yu-Hang Tang, Hongyi Liu, Guang Lin*, Huai Sun, Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of Molecules, J. Phys. Chem. A, 125, 20, 4488–4497, 2021.

https://doi.org/10.1021/acs.jpca.1c02391

 

[P152] Xiaolong Hu, Liangsheng Shi, Guang Lin*, The data-driven solution of energy imbalance-induced structural error in evapotranspiration models, Journal of Hydrology, 597: 126205, 2021.

https://doi.org/10.1016/j.jhydrol.2021.126205

 

[P151] Xin Cai, Guang Lin, Jinglai Li, Bayesian inverse regression for supervised dimension reduction with small datasets, Journal of Statistical Computation and Simulation, 91(14), 2817-2832, 2021.

https://doi.org/10.1080/00949655.2021.1909025

 

[P150] Guangshuai Han, Yixuan Sun, Guang Lin*, Na Lu, Machine learning regression guided thermoelectric materials discovery – A review, ES Materials & Manufacturing, 14, 20-35, 2021.

https://doi.org/10.30919/esmm5f451

 

[P149] Ziyang Huang, Guang Lin*, Arezoo M. Ardekani, A consistent and conservative model and its scheme for N-phase-M-component incompressible flows, Journal of Computational Physics, 434: 110229, 2021.

https://doi.org/10.1016/j.jcp.2021.110229

 

[P148] Frederick W. Damen, David T. Newton, Guang Lin*, Craig J. Goergen, Machine learning driven contouring of high-frequency four-dimensional cardiac ultrasound data, Applied Sciences, 11(4), 1690, 2021.

https://doi.org/10.3390/app11041690

 

[P147] Yating Wang, Wei Deng, Guang Lin*, Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications, Journal of Computational Physics, 432: 110134, 2021.

https://doi.org/10.1016/j.jcp.2021.110134

 

[P146] Yating Wang, Wei Deng, Guang Lin*, An adaptive Hessian approximated stochastic gradient MCMC method, Journal of Computational Physics, 432: 110150, 2021.

https://doi.org/10.1016/j.jcp.2021.110150

 

[P145] Shichao Zhou, Guang Lin*, Qinfang Qian, Chao Xu, Binary classification of floor vibrations for human activity detection based on dynamic mode decomposition, Neurocomputing, 432, 227-239, 2021.

https://doi.org/10.1016/j.neucom.2020.12.066

 

[P144] Yuepeng Wang, Xuemei Ding, Kun Hu, Fangxin Fang, I. M. Navon, Guang Lin*, Feasibility of DEIM for retrieving the initial field via dimensionality reduction, Journal of Computational Physics, 429, 110005, 2021.

https://doi.org/10.1016/j.jcp.2020.110005

 

[P143] Sheng Zhang, Guang Lin*, Robust subsampling-based threshold sparse Bayesian regression to tackle high noise and outliers for data-driven discovery of differential equations, Journal of Computational Physics, 428: 109962, 2021.

https://doi.org/10.1016/j.jcp.2020.109962

 

[P142] Hugo Raul Esquivel, Arun Prakash, Guang Lin*, Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems, Journal of Computational Physics, 430, 110044, 2021.

https://doi.org/10.1016/j.jcp.2020.110044

 

Journal Paper from 2020

 

[P141] Yixiang Deng, Xiu Yang, Guang Lin*, Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression, Communications in Computational Physics, 28 (2020), pp. 1812-1837. 2020.

https://arxiv.org/abs/2008.01066

 

[P140] Bangde Liu, Guang Lin*, High-dimensional Nonlinear Multi-fidelity model with Gradient-free Active Subspace Method, Communications in Computational Physics, Vol. 28, No. 5, pp. 1937-1969, 2020.

https://doi.org/10.4208/cicp.OA-2020-0195

 

[P139] Yating Wang, Guang Lin*, MFPC-Net: Multi-fidelity Physical Constrained Neural Process, CSIAM Transactions on Applied Mathematics, 1(4), 715-739, 2020.

https://global-sci.com/article/82393/mfpc-net-multi-fidelity-physics-constrained-neural-process

 

[P138] Nathan Arndt, Austin Biondi, Maria Castillo, Ranis Ibragimov, Guang Lin*, Vesselin Vatchev, Sheng Zhang, Energy spectrum of linear internal wave field in the vicinity of continental slope, Journal of Applied Mathematics and Physics, 8(10), 2256-2274, 2020.

https://doi.org/10.4236/jamp.2020.810169

 

[P137] Guang Lin*, Na Lu, Integrating Artificial Intelligence, Theory, Modeling and Experiments – Perspectives, Challenges, and Opportunities in Materials and Manufacturing, Journal of ES Materials and Manufacturing, 9:1-2, 2020.

https://doi.org/10.30919/esmm5f915

 

[P136] Lang Zhao, Tyler Tallman, Guang Lin*, Real-Time  Precise damage characterization in self-sensing materials via neural network-aided electrical Impedance tomography: A computational study, ES Materials & Manufacturing, 12:78-88, 2020.

https://doi.org/10.30919/esmm5f919

 

[P135] Jiuhai Chen, Lulu Kang, Guang Lin, Gaussian process assisted active learning of physical laws, Technometrics, 63(3): 329-342, 2020.

https://www.tandfonline.com/doi/full/10.1080/00401706.2020.1817790

[P134] Na Ou, Lijian Jiang, Guang Lin*, A low-rank approximated multiscale method for PDEs with random coefficients, SIAM Multiscale Modeling & Simulations, 18 (4), 1595-1620, 2020.

https://epubs.siam.org/doi/10.1137/19M1288565

 

[P133]       Ziyang Huang, Guang Lin*, Arezoo M. Ardekani, A consistent and conservative scheme for incompressible two-phase flows using the conservative Allen-Cahn model，Journal of Computational Physics, 420:109718, 2020.

https://doi.org/10.1016/j.jcp.2020.109718

 

[P132] Zhilong Huang, Yanping Tian, Chunjiang Li, Guang Lin, Yong Wang, Data-driven automated discovery of variational laws hidden in physical systems, Journal of the Mechanics and Physics of Solids, 137:103871, 2020.

https://doi.org/10.1016/j.jmps.2020.103871

 

[P131]       Wu Ma, Guang Lin*, Jingjing Liang, Estimating dynamics of central hardwood forests using random forests, Ecological Modelling, vol. 419, 108947, 2020.

https://doi.org/10.1016/j.ecolmodel.2020.108947

 

[P130]       Zhaopeng Hao, Guang Lin*, Zhongqiang Zhang, Error estimates of a spectral Petrov-Galerkin method for two-sided fractional reaction-diffusion equations, Applied Mathematics and Computation, 374, 125045, 2020.

https://doi.org/10.1016/j.amc.2020.125045

 

[P129]       Jiangjiang Zhang, Jasper A. Vrugt, Xiaoqing Shi, Guang Lin, Laosheng Wu, Lingzao Zeng. Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman-Inspired Proposal Distribution, Water Resources Research, Vol 56 (3), e2019WR025474, 2020.

https://doi.org/10.1029/2019WR025474

 

[P128]       Georgios Karagiannis, Wenrui Hao, Guang Lin*, Calibrations and validations of biological models with an application on the renal fibrosis, International Journal for Numerical Methods in Biomedical Engineering, 36(5), e3329, 2020.

https://doi.org/10.1002/cnm.3329

 

[P127]       Yu Huang, Yixing Ding, Qingshan Xu, Guang Lin*, Pengwei Du, Efficient Uncertainty Quantification in Economic Re-dispatch under High Wind Penetration Considering Interruptible Load, International Journal of Electrical Power and Energy Systems, 121:106104, 2020.

https://doi.org/10.1016/j.ijepes.2020.106104

 

[P126]       Moonseop Kim, Huayi Yin, Guang Lin*, Multi-fidelity Gaussian process regression for multiscale data integration for silicon nanowires, Theoretical and Applied Mechanics Letters, 10, 195-201, 2020.

https://pubs-en.cstam.org.cn/data/article/taml/preview/pdf/TAML-20-016.pdf

 

[P125]       Wenrui Hao, Jan S. Hesthaven, Guang Lin and Bin Zheng. A homotopy method with adaptive basis selection for computing multiple solutions of differential equations, Journal of Scientific Computing, 82, 19, 2020.

https://doi.org/10.1007/s10915-020-01123-1

 

[P124]       Yating Wang, Guang Lin*, Efficient deep learning techniques for multiphase flow simulation in heterogeneous porous media, Journal of Computational Physics, 401, 108969, 2020.

https://doi.org/10.1016/j.jcp.2019.108968

 

[P123]       Tehuan Chen, Zhigang Ren, Guang Lin, Z. Wu, B. Ye, Real-time computational optimal control of an MHD flow system with parameter uncertainty quantification, Journal of The Franklin Institute, 357 (5), 2830-2850, 2020. https://doi.org/10.1016/j.jfranklin.2019.12.013

 

[P122]       Samy Tindel, Yanghui Liu, Guang Lin, On the anticipative nonlinear filtering problem and its stability, Appl Math Optim, 2020.

https://doi.org/10.1007/s00245-019-09649-z

 

[P121]       Ziyang Huang, Guang Lin*, Arezoo M. Ardekani, Consistent, essentially conservative and balanced-force Phase-Field method to model incompressible two-phase flows, Journal of Computational Physics, 406, 109192, 2020.

https://doi.org/10.1016/j.jcp.2019.109192

 

[P 120]      Yiqi Gu, Xi Yang, Mengjiao Peng, Guang Lin*, Robust weighted SVD-type latent factor models for rating prediction, Expert Systems with Applications, Vol 141, 112885, 2020.

https://doi.org/10.1016/j.eswa.2019.112885

 

[P119] Sangpil Kim, Nicholas D. Winovich, Hyung-gun Chi, Guang Lin, Karthik Ramani, Latent transformations neural network for object view synthesis, The Visual Computer, 36, 1663–1677 2020.

https://doi.org/10.1007/s00371-019-01755-x

 

Journal Paper from 2019

[P118]       Xinxin Dong, Yongfeng Shen, Tingwei Yin, Raja Devesh Kumar Misra, Guang Lin, Strengthening a medium-carbon steel to 2800 MPa by tailoring nanosized precipates and the phase ratio, Materials Science and Engineering: A, 759: 725-735, 2019.

https://doi.org/10.1016/j.msea.2019.05.076

 

[P117]       Yu Huang, Qingshan Xu, Cheng Hu, Yixuan Sun, Guang Lin*, Probabilistic state estimation approach for AC/MTDC Distribution system using deep belief network with non-Gaussian uncertainties, IEEE Sensors Journal, 19(20): 9422 - 9430, 2019.

 https://doi.org/10.1109/JSEN.2019.2926089

 

[P116]       Na Ou, Lijian Jiang, Guang Lin*, A new bifidelity model reduction method for Bayesian inverse problems, International Journal for Numerical Methods in Engineering, 119(10): 941-963, 2019.

https://doi.org/10.1002/nme.6079

 

[P115]       Joan Ponce, Yiqiang Zheng, Guang Lin*, Zilan Feng, Assessing the effects of modeling the spectrum of clinical symptoms on the dynamics and control of Ebola, Journal theoretical biology, 467:111-122, 2019.

https://www.sciencedirect.com/science/article/abs/pii/S002251931930013X

 https://doi.org/10.1016/j.jtbi.2019.01.013

 

[P114]       Zhaopeng Hao, Moongyu Park, Guang Lin, Zhiqiang Cai, Finite element method for two-sided fractional elliptic differential equations with variable coefficients: Galerkin approach, Journal of Scientific Computing, 79(2): 700-717, 2019.

https://link.springer.com/article/10.1007/s10915-018-0869-5

 

[P113]       Yuepeng Wang, Lanlan Ren, Zongyuan Zhang, Guang Lin*, Chao Xu, Sparsity-promoting Elastic Net method with Rotation for High-Dimensional Nonlinear Inverse problem, Computer Methods in Applied Mechanics and Engineering, 345: 263-282, 2019.

https://doi.org/10.1016/j.cma.2018.10.040

 

[P112]       Jing Li, Guang Lin*, Yu Huang, Decentralized dynamic power management with local information, Elektronika Ir Elektrotechnika, Vol 25 No. 1, 36-43, 2019.

https://doi.org/10.5755/j01.eie.25.1.22734

 

[P111]       Moonseop Kim, Nicholas Winovich, Guang Lin*, Wontae Jeong, Peri-Net: Analysis of crack patterns using deep neural networks, Journal of peridynamics and nonlocal modeling, 1, 131-142,  2019.

https://doi.org/10.1007/s42102-019-00013-x

 

[P110]       Jun Yang, Wei Wang, Guang Lin*, Qing Li, Yeqing Sun, Yixuan Sun, Infrared Thermal Imaging-Based Crack Detection Using Deep Learning, IEEE Access, vol. 7, pp. 182060-182077, 2019.

 https://doi.org/10.1109/ACCESS.2019.2958264

 

[P109]       Yuepeng Wang, Kui Hu, Lanlan Ren, Guang Lin*, Optimal observations-based retrieval of topography in 2D shallow water equations using PC-EnKF, Journal of Computational Physics, 382: 43-60, 2019.

https://doi.org/10.1016/j.jcp.2019.01.004

 

[P108]       Nicholas D. Winovich, Karthik Ramani, Guang Lin*, ConvPDE-UQ: Fast convolutional encoder-decoder networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains, Journal of Computational Physics, 394:263-279, 2019. 

https://doi.org/10.1016/j.jcp.2019.05.026

 

[P107]       Ziyang Huang, Guang Lin*, Arezoo Ardekani, A Mixed Upwind/Central WENO Scheme for Incompressible Two-Phase Flows, Journal of Computational Physics, 387: 455-480, 2019.

https://doi.org/10.1016/j.jcp.2019.02.043

 

 

Journal Paper from 2018

 

[P106]       Yuepeng Wang, Yue Cheng, Zhongyuan Zhang, Guang Lin*, Calibration of reduced-order model for the coupled Burgers equations based on PC-EnKF, special issue in discontinuities and shock waves in various mathematical models, Mathematical Modelling of Natural Phenomena, 13 (2):21-39, 2018.

https://doi.org/10.1051/mmnp/2018023

 

[P105]       Jichun Li, Zhiwei Fang, Guang Lin*, Regularity analysis of metamaterial Maxwell’s equations with random coefficients and initial conditions, Computer Methods in Applied Mechanics and Engineering, 335: 24-51, 2018.

https://doi.org/10.1016/j.cma.2018.02.012

 

[P104]       Jinping Fang, Guang Lin*, and Hui Wan, Analysis of a stage-structured dengue model, Discrete and Continuous Dynamical Systems Series B, 23(9): 4045-4061, 2018.

https://doi.org/10.3934/dcdsb.2018125

 

[P103]       Jiangjiang Zhang, Guang Lin*, Weixuan Li, Laosheng Wu, Lingzao Zeng, An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions, Water Resources Research, 54(3): 1716-1733, 2018.

https://doi.org/10.1002/2017WR020906

 

[P102]       Yu Huang, Qingshuan Xu, Xianqiang Jiang, Yang Yang, Guang Lin*, An analytic approach to probabilistic load flow incorporating correlation between non-Gaussian random variables, Elektronika Ir Elektrotechnika, Vol 24 No. 3, 2018.

https://doi.org/10.5755/j01.eie.24.3.20980

 

[P101]       Dong-Ke Sun, Zhen-Hua Chai, Qian Li, Guang Lin*, A lattice Boltzmann – cellular automaton study on dendrite growth with melt convection in solidification of ternary alloys, Chinese Physics B. 27(8): 088105, 2018.

https://doi.org/10.1088/1674-1056/27/8/088105

 

[P100]       Yingwei Wang, Wenrui Hao, Guang Lin*, Two-level spectral methods for nonlinear differential equations with multiple solutions, SIAM Journal on Scientific Computing, 40(4): B1180-B1205, 2018.

https://doi.org/10.1137/17M113767X

 

[P99]        Jiangjiang Zhang, Jun Man, Guang Lin, Laosheng Wu, Lingzao Zeng, Inverse modeling of hydrologic systems with adaptive multifidelity Markov Chain Monte Carlo simulations, Water Resources Research, 54(7), 4867-4886, 2018.

https://doi.org/10.1029/2018WR022658

 

[P98]        Sheng Zhang, Guang Lin*, Robust data-driven discovery of governing physical laws with error bars, Proceedings of the Royal Society of London. Series A, mathematical, physical and engineering sciences, A 474: 20180305, 2018.

https://doi.org/10.1098/rspa.2018.0305

 

[P97]        Yifan Du, Guang Lin*，Turbulence Generation from a stochastic wavelet model, Proceeding of the Royal Society of London. Series A, mathematical, physical and engineering sciences, 474(2217):20180093, 2018.

https://doi.org/10.1098/rspa.2018.0093

 

[P96]        Yu Huang, Qingshan Xu, Guang Lin*, Congestion Risk Averse Stochastic Unit Commitment with Transmission Reserves in Wind-Thermal Power Systems, Applied Sciences, 8(10), 1726, 2018.

https://doi.org/10.3390/app8101726

 

[P95]        Jing Li, Na Ou, Guang Lin*, Wei Wei, Compressive Sensing based Stochastic Economic Dispatch with High Penetration Renewables, IEEE Transactions on Power Systems, P(99): 1-1, 2018.

https://doi.org/10.1109/TPWRS.2018.2874718

 

[P94]        Yu Huang, Qingshan Xu, Sajjad Abedi, Xianqiang Jiang, Guang Lin*, Stochastic security assessment for power systems with high renewable energy penetration considering frequency regulation, IEEE Access, 7: 6450-6460, 2018.

https://doi.org/10.1109/ACCESS.2018.2880010

 

Journal Paper from 2017

 

[P93]        Xiaoliang Wan, Bin Zheng, Guang Lin, An hp-adaptive minimum action method based on a posteriori error estimate, Communications in Computational Physics, 23(2): 408-439, 2017.

https://doi.org/10.4208/cicp.OA-2017-0025

 

[P92]        Zhaopeng Hao, Wanrong Cao, and Guang Lin*, A second-order difference scheme for the time fractional substantial diffusion equation, Journal of Computational and Applied Mathematics, Vol 313, 54-69, 2017.

https://doi.org/10.1016/j.cam.2016.09.006

 

[P91]        Luoping Chen, Bin Zheng, Guang Lin*, Nikolaos Voulgarakis, A two-level stochastic collocation method for semilinear elliptic equations with random coefficients, Journal of Computational and Applied Mathematics, Vol 315, 195-207, 2017.

https://doi.org/10.1016/j.cam.2016.10.030

 

[P90]        Junpeng Wang, Xiaotong Liu, Hanwei Shen, Guang Lin, Multi-resolution Climate Ensemble parameter analysis with nested parallel coordinates plots, IEEE Transactions on Visualization and Computer Graphics, Vol 23, Issue 1, 81-90, 2017. https://doi.org/10.1109/TVCG.2016.2598830

 

[P89]        Ayan Biswas, Guang Lin, Hanwei Shen, Visualization of Time-Varying Weather Ensembles Across Multiple Resolutions, IEEE Transactions on Visualization and Computer Graphics, Vol 23, Issue 1, 841-850, 2017.

https://doi.org/10.1109/TVCG.2016.2598869

 

[P88]        Jiangjiang Zhang, Weixuan Li, Guang Lin, Lingzao Zeng, Laosheng Wu, Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method, Water Resources Research, 53(3): 1948-1962, 2017.

https://doi.org/10.1002/2016WR019518

 

[P87]        Zuyuan Wang, Salar Safarkhani, Guang Lin, Xiulin Ruan, Uncertainty quantification of thermal conductivities from equilibrium molecular dynamics simulations, International Journal of Heat and Mass Transfer, 112: 267-278, 2017.

https://doi.org/10.1016/j.ijheatmasstransfer.2017.04.077

 

[P86]        Ranis N. Ibragimov, Guang Lin, Nonlinear Analysis of Perturbed Rotating Whirlpools in the Ocean and Atmosphere, Mathematical Modelling of Natural Phenomena, 12(1): 94-114, 2017.

https://doi.org/10.1051/mmnp/201712106

 

[P85]        Georgios Karagiannis, Guang Lin*, On the design of a predictive model of computer model mixtures and their calibration through experimental data, Journal of Computational Physics, 342: 139-160, 2017.

https://doi.org/10.1016/j.jcp.2017.04.003

 

[P84]        Emilie Hogan, Eduardo Cotilla-Sanchez, Mahantesh Halappanavar, Zhenyu Huang, Guang Lin, Shuai Lu, Shaobu Wang, Comparative Studies of Clustering Techniques for Real-Time Dynamic Model Reduction, Statistical Analysis and Data Mining, 10(5): 263-276, 2017.

https://doi.org/10.1002/sam.11352

 

[P83]        Georgios Karagiannis, Bledar A. Konomi, Guang Lin*, On the Bayesian calibration of expensive computer models with input dependent parameters, special issue in Spatial Statistics journal on Spatio-temporal and Geostatistical Analysis of Hydrological Events and Related Hazards, Spatial Statistics, 34, 100258, 2019.

https://doi.org/10.1016/j.spasta.2017.08.002

 

[P82]        Mu Wang, Guang Lin, Alex Pothen, Using automatic differentiation for compressive sensing in uncertainty quantification, Optimization Methods & Software, 0(0): 1-14, 2017.

https://doi.org/10.1080/10556788.2017.1359267

 

[P81]        Bledar A. Konomi, Georgios Karagiannis, Kevin Lai, Guang Lin*, Bayesian treed Calibration: an application to Carbon capture with AX sorbent, Journal of American Statistical Association, 112(517): 37-53, 2017.

https://doi.org/10.1080/01621459.2016.1190279

 

[P80]        G. Karagiannis, B. Konomi, G. Lin*, F. Liang, Parallel and Interacting Stochastic Approximation Annealing for Global Optimization, Stat. Comput., 27, 927-945, 2017.

https://doi.org/10.1007/s11222-016-9663-0

 

Journal Paper from 2016

 

[P79]        Li Li, Yongqing Yang, Guang Lin, The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control, Neural Computing and Applications, 27(2): 447-457, 2016.

https://doi.org/10.1007/s00521-015-1865-4

 

[P78]        Xiu Yang, Huan Lei, Nathan Baker, Guang Lin*, Enhancing sparsity of Hermite polynomial expansions by iterative rotations, Journal of Computational Physics, 307: 94-09, 2016.

https://doi.org/10.1016/j.jcp.2015.11.038

 

[P77]        Huan Lei, Xiu Yang, Bin Zheng, Guang Lin*, N. Baker, Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation, SIAM Multiscale Modeling and Simulation, 13(4): 1327-1353, 2016.

https://doi.org/10.1137/140981587

 

[P76]        Yuzhou Sun, Pengtao Sun, Bin Zheng, Guang Lin*, Error analysis of finite element method for Poisson-Nernst-Planck equations, Journal of Computational and Applied Mathematics, 301: 28-43, 2016.

https://doi.org/10.1016/j.cam.2016.01.028

 

[P75]        Zhijie Xu, Ramakrishna Tipireddy, Guang Lin, Analytical Approximation and Numerical Studies of One-dimensional Elliptic Equation with Random Coefficients, Applied Mathematical Modelling, 40 (9-10): 5542-5559, 2016.

https://doi.org/10.1016/j.apm.2015.12.041

 

[P74]        H. Wang, G. Lin*, J. Li, Gaussian process surrogates for failure detection: a Bayesian experimental design approach, Journal of Computational Physics, 313: 247-259, 2016.

https://www.sciencedirect.com/science/article/abs/pii/S002199911600125X

 

[P73]        I. Bright, G. Lin*, N. Kutz, Classification of Spatio-temporal Data via Asynchronous sparse sampling: Application to flow around a cylinder, SIAM Multiscale modeling and simulation, 14(2), 823–838, 2016.

https://epubs.siam.org/doi/10.1137/15M1023609

 

[P72]        Z. Zhang, X. Yang, G. Lin*, POD-based constrained sensor placement and field reconstruction from noisy wind measurement: A perturbation study, Mathematics, 4, 26; 2016.

https://doi.org/10.3390/math4020026

 

[P71]        Q. Liao, G. Lin*, Reduced basis ANOVA method for partial differential equation with high-dimensional random inputs, Journal of Computational Physics, 317: 148-164, 2016.

https://www.sciencedirect.com/science/article/abs/pii/S0021999116300754

 

[P70]        W. Guo, G. Lin*, A. J. Christlieb, J. Qiu, An adaptive WENO collocation method for differential equations with random coefficients, Mathematics, 4(2), 29, 2016.

https://doi.org/10.3390/math4020029

 

[P69]        Victor Ginting, Guang Lin, Jiangguo Liu, On Application of the Weak Galerkin Finite Element Method to a Two-phase Model for Subsurface Flow, Journal of Scientific Computing, 66(1): 225-239, 2016.

https://doi.org/10.1007/s10915-015-0021-8

 

[P68]        W. Li, G. Lin*, B. Li, Inverse regression-based uncertainty quantification algorithms for high-dimensional models in theory and practice, Journal of Computational Physics, 321:259-278, 2016.

https://www.sciencedirect.com/science/article/abs/pii/S0021999116301851

 

[P67]        R.J. Leveque, K. Waagan, F.I. Gonzalez, D. Rim, G. Lin*, Generating random earthquake events for probabilistic tsunami hazard assessment, Pure and Applied Geophysics, 173, 3671-3692, 2016.

https://link.springer.com/article/10.1007/s00024-016-1357-1

 

Journal Paper from 2015

 

[P66]        B. Konomi, G. Karagiannis, G. Lin*, On the Bayesian Treed Multivariate Gaussian Process with Linear Model of Coregionalization, Journal of Statistical Planning and Inference, 157-158: 1-15, 2015.

https://www.sciencedirect.com/science/article/abs/pii/S0378375814001608

 

[P65]        R.N. Ibragimov, G. Lin*, Splitting phenomenon of a higher-order shallow water theory associated with a longitudinal planetary waves, Dynamics of Atmospheres and Oceans, 69: 1-11, 2015.

https://www.sciencedirect.com/science/article/abs/pii/S0377026514000542

 

[P64]        G. Karagiannis, B. Konomi, G. Lin*, A Bayesian mixed shrinkage prior procedure for spatial-stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs, Journal of Computational Physics, 284: 528-546, 2015.

https://www.sciencedirect.com/science/article/abs/pii/S0021999114008511

 

[P63]        W. Hao, Z. Xu, C. Liu, G. Lin*, A Fictitious Domain Method with a Hybrid Cell Model for Simulating Motion of Cells in Fluid Flow, Journal of Computational Physics, 280: 345-362, 2015.

https://www.sciencedirect.com/science/article/abs/pii/S0021999114006561

 

[P62]        R. N. Ibragimov, G. Lin*, Longitudinal Dimensions of Polygon-shaped Planetary Waves, Journal of Applied Nonlinear Dynamics 4(2): 153-167, 2015.

https://doi.org/10.5890/JAND.2015.06.005

 

[P61]        Bao J, Z Hou, Y Fang, H Ren, and G. Lin*, Uncertainty quantification for evaluating the impacts of fracture zone on pressure buildup and ground surface uplift during geological CO2 sequestration, Greenhouse Gases: science and technology, 5(3):254-267, 2015.

https://scijournals.onlinelibrary.wiley.com/doi/full/10.1002/ghg.1456

 

[P60]        W. Li and G. Lin*, Adaptive Importance Sampling from Multimodal Distributions using Polynomial Chaos Surrogates and Gaussian Mixture Proposal, Journal of Computational Physics, 294: 173-190, 2015.

https://www.sciencedirect.com/science/article/abs/pii/S0021999115002028

 

[P59]        J. Li, G. Lin*, X. Yang, A Frozen Gaussian Approximation-based Multi-level Particle Swarm Optimization for Seismic Inversion, Journal of Computational Physics, 296: 58-71, 2015.

https://www.sciencedirect.com/science/article/abs/pii/S0021999115003071

 

[P58]        B. Konomi, G. Lin*, Low-Cost Multi-output Gaussian Process with Application to Uncertainty Quantification, International Journal for Uncertainty Quantification, 5(4): 375-392, 2015.

https://doi.org/10.1615/Int.J.UncertaintyQuantification.2015007941

 

[P57]        E. Sousa, G. Lin*, U. Shumlak, Uncertainty Quantification of the GEM Challenge Magnetic Reconnection Problem using the Multi-level Monte Carlo Method, International Journal for Uncertainty Quantification, 5(4): 327-339, 2015.

https://doi.org/10.1615/Int.J.UncertaintyQuantification.2015006492

 

[P56]        G. Herschlag, S. Mitran, G. Lin*, A consistent hierarchy of generalized kinetic equation approximations to the master equation applied to surface catalysis, Journal of Chemical Physics, 142: 234703, 2015.

https://pubs.aip.org/aip/jcp/article-abstract/142/23/234703/193644/A-consistent-hierarchy-of-generalized-kinetic

 

[P55]        B. Zhang, B. Konomi, H. Sang, G. Karagiannis, G. Lin*, Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions, Journal of Computational Physics, 300: 623–642, 2015.

https://www.sciencedirect.com/science/article/abs/pii/S0021999115005239

 

[P54]        Z. Hao, G. Lin, Z. Sun, A high-order difference scheme for the fractional sub-diffusion equation, International Journal of Computer Mathematics, 94(2), 405-426, 2015.

https://www.tandfonline.com/doi/full/10.1080/00207160.2015.1109642

 

Journal Paper from 2014

 

[P53]        J. Wei, G. Lin*, L. Jiang, Y. Efendiev, Analysis of Variance-based Mixed Multiscale Finite Element Method and Applications in Stochastic Two-Phase Flows, International Journal for Uncertainty Quantification, 4(6): 455-477, 2014.

https://doi.org/10.1615/Int.J.UncertaintyQuantification.2014006135

 

[P52]        S. Wang, S. Lu, N. Zhou, G. Lin*, M. Elizondo, M.A. Pai, Dynamic-feature Extraction, Attribution and Reconstruction (DEAR) Method for Power System Model Reduction, IEEE Transactions on Power Systems, vol. 29, no. 5, pp. 2049-2059, 2014.

https://ieeexplore.ieee.org/document/6730699

 

[P51]        G. Lin*, M. Elizondo, S. Lu, X. Wan, Uncertainty Quantification in Dynamic Simulations of Large-scale Power System Models using the High-Order Probabilistic Collocation Method on Sparse Grids, International Journal for Uncertainty Quantification, 4(3): 185-204, 2014.

https://doi.org/10.1615/Int.J.UncertaintyQuantification.2013003479

 

[P50]        Fang Z, Z Hou, G Lin, DW Engel, Y Fang, and PW Eslinger.  Exploring the Effects of Data Quality, Data Worth, and Redundancy of CO2 Saturation Data on Injection Reservoir Characterization through PEST Inversion, Environmental Earth Sciences, 71(7): 3025-3037, 2014.

https://link.springer.com/article/10.1007/s12665-013-2680-9

 

[P49]        G. Lin*, J. Bao, Z. Xu, A. M. Tartakovsky, and CH Henager, Jr., A Phase-Field Model Coupled with Lattice Kinetics Solver for Modeling Crystal Growth in Furnaces, Communications in Computational Physics, 15(1): 76-92, 2014.

https://doi.org/10.4208/cicp.300612.210313a

 

[P48]        G. Karagiannis, G. Lin*, Selection of Polynomial Chaos Bases via Bayesian Model Uncertainty Methods with Applications to Sparse Approximation of PDEs with Stochastic Inputs, Journal of Computational Physics, 259: 114–134, 2014.

https://www.sciencedirect.com/science/article/abs/pii/S002199911300778X

 

[P47]        B. Konomi, G. Karagiannis, A. Sarkar, X. Sun, G. Lin*, Bayesian Treed Multivariate Gaussian Process with Adaptive Design: Application to a Carbon Capture Unit, Technometrics, 56(2): 145–158, 2014.

https://www.tandfonline.com/doi/abs/10.1080/00401706.2013.879078

 

[P46]        D. Meng, B. Zheng, G. Lin*, M.L. Sushko, Numerical Solution of 3D Poisson-Nernst-Planck Equations Coupled with Classical Density Functional Theory for Modeling Ion and Electron Transport in a Confined Environment, Communications in Computational Physics, 16(5): 1298-1322, 2014.

https://www.doi.org/10.4208/cicp.040913.120514a

 

[P45]        D. Meng, Q. Zhang, X. Gao, S. Wu, G. Lin, LipidMiner: a software for automated identification and quantification of lipids from multiple liquid chromatography-mass spectrometry data files, Rapid Communications in Mass Spectrometry, 28 (8): 981-985, 2014.

https://doi.org/10.1002/rcm.6865

 

[P44]        W. Li, G. Lin*, D. Zhang, An Adaptive-ANOVA-based PCKF for High-Dimensional Nonlinear Inverse Modeling, Journal of Computational Physics, 258: 752–772, 2014.

https://www.sciencedirect.com/science/article/abs/pii/S002199911300781X

 

[P43]        G. Lin*, J. Liu, L. Mu, X. Ye, Weak Galerkin Finite Element Methods for Darcy Flow: Anisotropy and Heterogeneity, Journal of Computational Physics, 276: 422-437, 2014.

https://www.sciencedirect.com/science/article/abs/pii/S0021999114004793

 

[P42]        Z. Hou, D.W. Engel, D.H. Bacon, G. Lin, Y. Fang, H. Ren, Z. Fang, Uncertainty Analyses of CO2 Plume Expansion subsequent to Wellbore CO2 Leakage into Aquifers, International Journal of Greenhouse Gas Control, 27:69-80, 2014.

https://www.sciencedirect.com/science/article/abs/pii/S1750583614001273

 

[P41]        S. Shao, N. Abdolrahim, D. F. Bahr, G. Lin, and H.M. Zbib,  Stochastic Effects in Plasticity in Small Volumes, International Journal of Plasticity, 52: 117-132, 2014.

https://www.sciencedirect.com/science/article/abs/pii/S074964191300185X

 

[P40]        F. Liang, Y. Cheng, and G. Lin*, Simulated Stochastic Approximation Annealing for Global Optimization with a Square-Root Cooling Schedule, Journal of the American Statistical Association, 109(506): 847-863, 2014.

https://www.tandfonline.com/doi/abs/10.1080/01621459.2013.872993

 

[P39]        H. Yan, Y. Qian, G. Lin, L.R. Leung, B. Yang, Q. Fu, Parametric Sensitivity and Calibration for the Kain-Fritsch Convective Parameterization Scheme in the WRF Model, Climate Research, 59: 135-147, 2014.

https://doi.org/10.3354/cr01213

 

[P38]        Z. Zhang, X. Hu, T.Y. Hou*, G. Lin*, P. Yan, An adaptive ANOVA-based data-driven stochastic method for elliptic PDE with random coefficients, Communications in Computational Physics, 16(2): 571-598, 2014.

https://doc.global-sci.org/uploads/Issue/CiCP/v16n3/571_short.pdf

 

[P37]        G. Lin*, J. Liu, F. Sadre-Marandi, A comparative study on the weak Galerkin, discontinuous Galerkin, and mixed finite element methods, Journal of Computational and Applied Mathematics, 273: 346-362, 2015.

https://sciencedirect.com/science/article/pii/S0377042714003057

 

[P36]        X. Shi, G. Lin*, Modeling the Sedimentation of Red Blood Cells in Flow under Strong External Magnetic Body Force using a Lattice Boltzmann Fictitious Domain Method, Numerical Mathematics: Theory, Methods and Applications,7(4): 512-523, 2014.

https://www.osti.gov/biblio/1221504

 

[P35]        G. Lin*, J. Bao, Z. Xu, A three-dimensional phase field model coupled with lattice kinetics solver for modeling crystal growth in furnaces with accelerated crucible rotation and traveling magnetic field, Computers and Fluids, 103: 204-214, 2014.

https://www.sciencedirect.com/science/article/abs/pii/S0045793014003120

 

[P34]        M. J. Del Razo, W. Pan, H. Qian, G. Lin, Fluorescence Correlation Spectroscopy and Nonlinear Stochastic Reaction-Diffusion, Journal of Physical Chemistry B, 118 (25): 7037-7046, 2014.

https://pubs.acs.org/doi/10.1021/jp5030125

 

[P33]        Guo Z, M Wang, Y Qian, VE Larson, SJ Ghan, M Ovchinnikov, P Bogenschutz, C Zhao, G. Lin, and T Zhou, A Sensitivity Analysis of Cloud Properties to CLUBB Parameters in the Single Column Community Atmosphere Model (SCAM5), Journal of Advances in Modeling Earth Systems, 6 (3): 829-858, 2014.

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014ms000315

 

[P32]        G. Xu, G. Lin*, J. Liu, Rare Event Simulation for Stochastic Korteweg-de Vries Equation, SIAM/ASA Journal on Uncertainty Quantification, 2 (1): 698-716, 2014.

https://epubs.siam.org/doi/10.1137/130944473

 

Journal Paper from 2013

 

[P31]        Chun Zhao, Xiaohong Liu, Yun Qian, Jin-Ho Yoon, Zhangshuan Hou, Guang Lin, Sally A. McFarlane, Hailong Wang, Ben Yang, Po-Lun. Ma, Huiping Yan, Jie Bao, A Sensitivity Study of Radiative Fluxes at the Top of Atmosphere to Cloud-Microphysics and Aerosol Parameters in the Community Atmosphere Model CAM5, Atmos. Chem. Phys., 13: 10969-10987, 2013

https://acp.copernicus.org/articles/13/10969/2013/

 

[P30]        Jie Bao, Zhangshuan Hou, Yilin Fang, Huiying Ren, Guang Lin, Uncertainty quantification for evaluating impacts of caprock and reservoir properties on geomechanical responses during geologic CO2 sequestration, Greenhouse Gases: Science and Technology, 3(5): 338-358, 2013.

https://doi.org/10.1002/ghg.1362

 

[P29]        Xiaoliang Wan, Guang Lin*, Hybrid parallel computing of minimum action method, Parallel Computing, 39: 638-651, 2013.

https://doi.org/10.1016/j.parco.2013.08.004

 

[P28]        Zhangshuan Hou, David W Engel, Guang Lin, Yilin Fang, and Zhufeng Fang, An Uncertainty Quantification Framework for Studying the Effect of Spatial Heterogeneity in Reservoir Permeability on CO2 Sequestration, Mathematical Geosciences, 45(7): 799-817, 2013.

https://doi.org/10.1007/s11004-013-9459-0

 

[P27]        Jie Bao, Zhijie Xu, Guang Lin, Yilin Fang, Evaluating the impact of aquifer layer properties on geo-mechanical response during CO2 geological sequestration, Computers & Geosciences, 54: 28-37, 2013.

https://doi.org/10.1016/j.cageo.2013.01.015

 

[P26]        Seun Ryu, Guang Lin*, Xin Sun, Mohammad A Khaleel, and Dongsheng Li, Adaptive Multiple Super Fast Simulated Annealing for Stochastic Image Reconstruction, International Journal of Theoretical and Applied Multiscale Mechanics, 2(4): 287-297, 2013.

https://doi.org/10.1504/IJTAMM.2013.062161

 

[P25]        Katrina Hui, Guang Lin*, Wenxiao Pan, Understanding the Mechanisms of Sickle Cell Disease by Simulations with a Discrete Particle Model, Computational Science & Discovery, 6(1): 015004, 2013.

https://doi.org/10.1088/1749-4699/6/1/015004

 

[P24]        Zhongqiang Zhang, Xiu Yang, Guang Lin, G. Karniadakis, Numerical solution of the Stratonovich- and Ito-Euler equations: Application to the stochastic piston problem, Journal of Computational Physics, 236: 15-27, 2013.

https://doi.org/10.1016/j.jcp.2012.11.017

 

[P23]        Xin Shi, Guang Lin*, Jianfeng Zhou, Dmitry A. Fedosov, A Lattice Boltzmann Fictitious Domain Method for Modeling Red Blood Cell Deformation and Multiple-Cell Hydrodynamic Interaction in Flow, International Journal for Numerical Methods in Fluids, 72 (8): 895-911, 2013.

https://doi.org/10.1002/fld.3764

 

[P22]        Ilias Billions, Nicholas Zabaras, Bledar A. Konomi, and Guang Lin.  Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification, Journal of Computational Physics, 241: 212-239, 2013.

https://doi.org/10.1016/j.jcp.2013.01.011

 

[P21]        Ben Yang, Yun Qian, Guang Lin, L. Ruby Leung, Philip J. Rasch, Guang j. Zhang, Sally A. McFarlane, Chun Zhao, Yaocun Zhang, Hailong Wang, Minghuai Wang, and Xiaohong Liu, Uncertainty Quantification and Parameter Tuning in the CAM5 Zhang-McFarlane Convection Scheme and Physical Impact of Improved Convection on the Global Circulation and Climate, Journal of Geophysical Research. D. (Atmospheres), 118: 395-415, 2013.

https://doi.org/10.1029/2012JD018213

 

[P20]        Ido Bright, Guang Lin*, Nathan Kutz, Compressive Sensing Based Machine Learning Strategy for Characterizing The Flow Around A Cylinder With Limited Pressure Measurements, Physics of Fluids, 25: 127102, 2013.

https://doi.org/10.1063/1.4836815

 

Journal Paper from 2012

 

[P19]        Zhangshuan Hou, Maoyi Huang, L. Ruby Leung, Guang Lin, and Daniel M. Ricciuto, Sensitivity of Surface Flux Simulations to Hydrologic Parameters Based on an Uncertainty Quantification Framework Applied to the Community Land Model, Journal of Geophysical Research. D. (Atmospheres), 117(D15): D15108, 2012.

https://doi.org/10.1029/2012JD017521

 

[P18]        Ben Yang, Yun Qian, Guang Lin, Ruby Leung, Yaocun Zhang, Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model, Atmospheric Chemistry and Physics, 12(5): 2409-2427, 2012. 

https://doi.org/10.5194/acp-12-2409-2012

 

[P17]        Ariel Balter, Guang Lin*, and Alexandre M. Tartakovsky, The Effect of Nonlinearity in Hybrid Kinetic Monte Carlo-Continuum models, Physical Review E, 85: 016707, 2012.

https://doi.org/10.1103/PhysRevE.85.016707

 

[P16]        Xiu Yang, Minseok Choi, Guang Lin, George E. Karniadakis, Adaptive ANOVA Decomposition of Incompressible and Compressible Flows, Journal of Computational Physics, 231(4): 1587–1614, 2012.

https://doi.org/10.1016/j.jcp.2011.10.028

 

Journal Paper from 2011

 

[P15]        Zhiliang Xu, Yingjie Liu, Huijing Du, Guang Lin*, and Chi-Wang Shu, Point-wise Hierarchical Reconstruction for Discontinuous Galerkin and Finite Volume Methods for Solving Conservation Laws, Journal of Computational Physics, 230 (17): 6843-6865, 2011.

https://doi.org/10.1016/j.jcp.2011.05.014

 

[P14]        Donghai Mei, Guang Lin. Effects of heat and mass transfer on the reaction kinetics of CO oxidation on the RuO2(110) catalyst, Catalysis Today, 165: 56-63, 2011.

https://doi.org/10.1016/j.cattod.2010.11.041

 

Journal Paper from 2010

 

[P13]        Guang Lin*, Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids, Journal of Computational Physics, 229(19): 6995-7012, 2010.

https://doi.org/10.1016/j.jcp.2010.05.036

 

[P12]        Guang Lin* and Alexandre M. Tartakovsky, Numerical studies of three-dimensional stochastic Darcy's equation and stochastic advection-diffusion-dispersion equation, Journal of Scientific Computing, 43(1): 92-117, 2010.

https://doi.org/10.1007/s10915-010-9346-5

 

Journal Paper from 2009

 

[P11]        Guang Lin* and Alexandre M. Tartakovsky, An efficient, high-order probabilistic collocation method on sparse grids for three-dimensional flow and solute transport in randomly heterogeneous porous media, Advances in Water Resources,  32(5): 712-722, 2009.

https://doi.org/10.1016/j.advwatres.2008.09.003.

 

[P10]        Guang Lin and George E. Karniadakis, Sensitivity Analysis and Stochastic Simulations of Non-equilibrium Plasma Flow, International Journal for Numerical Methods in Engineering, 80(6-7): 738 – 766, 2009.

https://doi.org/10.1002/nme.2582

 

[P9] Wangyi Wu, Guang Lin, Basic function scheme of polynomial type, Applied Mathematics and Mechanics, 30, 1091–1103, 2009.

https://doi.org/10.1007/s10483-009-0903-y

 

[P8] Zhiliang Xu and Guang Lin*,  Hierarchical reconstruction for spectral/hp element methods for solving hyperbolic conservation laws, Acta Mathematica Scientia, 29(6): 1737-1748, 2009.

https://doi.org/10.1016/S0252-9602(10)60014-8

 

Journal Paper from 2008

 

[P7] Guang Lin, Chau-Hsing Su and George E. Karniadakis, Stochastic modeling of random roughness in shock scattering problems: Theory and simulations, Comput. Methods Appl. Math. Eng., 197(43-44): 3420-3434, 2008.

https://doi.org/10.1016/j.cma.2008.02.025

 

Journal Paper from 2007

 

 [P6] Guang Lin, Xiaoliang Wan, Chau-Hsing Su and George E. Karniadakis, Stochastic computational fluid mechanics, IEEE Computing in Science and Engineering (CiSE), 9:21-29, 2007.

https://doi.org/10.1109/MCSE.2007.38

 

[P5] Guang Lin, Chau-Hsing Su and George E. Karniadakis, Random Roughness Enhances Lift in Supersonic Flow, Physical Review Letters, 99:104501, 2007.

https://doi.org/10.1103/PhysRevLett.99.104501

 

Journal Paper from 2006

 

[P4] Guang Lin and Leopold Grinberg and George E. Karniadakis, Numerical studies of the stochastic Korteweg-de Vries equation, Journal of Computational Physics, 213(2): 676-703, 2006.

https://doi.org/10.1016/j.jcp.2005.08.029

 

[P3] Guang Lin and George E. Karniadakis, A discontinuous Galerkin method for two-temperature plasmas, Computer Methods in Applied Mechanics and Engineering, special issue in Discontinuous Galerkin Methods. 195(25-28): 3504-3527, 2006.

https://doi.org/10.1016/j.cma.2005.06.024

 

[P2] Guang Lin, Chau-Hsing Su and George E. Karniadakis, Predicting shock dynamics in the presence of uncertainties, Journal of Computational Physics, special issue in stochastic uncertainty prediction, 217(1) 260-276, 2006.

https://doi.org/10.1016/j.jcp.2006.02.009

 

Journal Paper from 2004

                                                                      

[P1] Guang Lin, Chau-Hsing Su and George E. Karniadakis, The stochastic piston problem, Proceedings of the National Academy of Sciences of the United States of America, 101(45):15840-15845, 2004.

https://doi.org/10.1073/pnas.0405889101

 

38 Submitted Papers in Archival Refereed Journals (* denotes corresponding author)      

 

[S38] Rajdeep Haldar, Yue Xin, Qifan Song, Guang Lin*, Adversarial Vulnerability as a Consequence of Manifold Inseparibility, in review.

https://arxiv.org/abs/2410.06921

 

[S37] Shuguang Chen, Guang Lin*, LLM Reasoning Engine: Specialized Training for Mathematical Reasoning, in review.

https://arxiv.org/abs/2412.20227

 

[S36] Jiahao Zhang, Shiqi Zhang, Guang Lin*, MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems, Journal of Computational Physics, in review.

 

[S35]Zachary McGuire, Sainitya Revuru, Sheng Zhang, Amanda Blankenberger, Moiz Rasheed, Jacob Hosen, Guang Lin, Mohit S Verma, Modelling complex growth profiles of Bacteroides fragilis and Escherichia coli on various carbohydrates in an anaerobic environment, in review.

https://www.biorxiv.org/content/10.1101/2023.05.01.538938v2

 

[S34] Gang Yang, Bledar A. Konomi, Jonathan Hobbs, Guang Lin, and Emily L. Kang, A Data Driven Statistical Emulation for Large-Scale Remote Sensing Observing Systems, Statistica Sinica, in review.

[S33] Tianqiao Zhao, Meng Yue, Jianhui Wang, Christian Moya, and Guang Lin, Feedback-corrected Deep Stochastic Learning for Koopman-based Online Predictive Control of Wind Farm under Uncertainties, IEEE Transactions on Industrial Informatics, in review.

 

[S32]        Yuqing Li, Tao Luo, Zheng Ma, Guang Lin, Nung Kwan Yip, Numerical Stability for Differential Equations with Memory, Journal of Computational Physics, in review, 2024.

https://arxiv.org/abs/2305.06571

 

[S31] Christian Moya, Guang Lin*, Tianqiao Zhao, Meng Yue, On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators, IEEE Neural Computing, in review.

https://arxiv.org/abs/2301.12538

 

[S30]        Yan Xiang, Yu-Hang Tang, Guang Lin*, Huai Sun, Predicting Thermodynamic and Transport Properties of Molecular Liquids Using Scalable Gaussian Processes and Marginalized Graph Kernel, American Chemistry Society Omega, in review.

 

[S29]        Zhaopeng Hao, Guang Lin*, Implicit finite difference schemes for multi-term time-fractional mixed diffusion-wave equations, Applied Mathematics and Computation, in review.

 

[S28]        Wenrui Hao, Long Chen, Guang Lin, Qing Nie, A. Sommese, Homotopy methods for studying complex patterns in parametric reaction-diffusion systems, SIAM Multiscale Modeling and Simulation, in review.

 

[S27] Haoyang Zheng, Guang Lin*, LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems, Advanced Science, in review.

https://arxiv.org/abs/2411.01719

 

[S26]        Guangshuai Han, Yen-Fang Su, Rui He, Cihang Huang, Zhihao Kong, Guang Lin, Yining Feng, and Na Lu, Are We Measuring Concrete Strength Correctly? AI Solutions for Real-Time Structural Monitoring, Nature Communication, in review.

[S25] Binghang Lu, Zhao-peng Hao, Christian Moya, Guang Lin*, fPINN-DeepONet: An operator learning framework for multi-term time-fractional mixed diffusion-wave equations, Journal of Computational Physics, in review.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5080263

[S24]        Yu Huang, Qingshan Xu, Guang Lin*, An improved cumulant-based method for stochastic risk assessment considering frequency regulation and renewable energy integration, Electric Power Systems Research, in review.

[S23]        Yifan Du, Wen Huang, Guang Lin*, Turbulence deconvolution using optimization on quotient manifold, Journal of Fluid Mechanics, in review.

 

[S22] Jiahao Zhang, Shiqi Zhang, Guang Lin*, PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations, Journal of Computational Physics, in review.

https://arxiv.org/abs/2204.02583

 

[S21]        Jiahao Zhang, Shiqi Zhang, Guang Lin*, RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification, Journal of Computational Physics, in review.

https://arxiv.org/abs/2204.04819

 

[S20]        Yixuan Sun, Jia Li, Guang Lin*, Lingzao Zeng, Permeability prediction of porous media with neural networks given three-dimensional structural images, Water Resources Research, in review.

 

[S19]        Yikai Liu, Ming Chen, Guang Lin*, Backdiff: A diffusion model for generalized transferable protein backmapping, Journal of Chemical Theory and Computation, 2024, in review.

https://arxiv.org/abs/2310.01768

 

[S18]        Kabir Oluwatobi Idowu, Abdullateef Adedeji, and Guang Lin*, A Semi-analytic Hybrid Approach for Solving the Buckmaster Equation using the Elzaki Projected Differential Transform Method (EPDTM), Engineering report, in review.

https://doi.org/10.22541/au.172198594.45321176/v1

 

[S17]        Guanxun Li, Guang Lin*, Zecheng Zhang, Quan Zhou, Fast Replica Exchange Stochastic Gradient Langevin Dynamics, Neurocomputing, in review.

https://arxiv.org/pdf/2301.01898

 

[S16]        Nick Winovich, Mitchell Daneker, Lu Lu, Guang Lin*, Active operator learning with predictive uncertainty quantification for partial differential equations, Journal of Computational Physics, in review.

 

[S15] Jiahao Zhang, Shiqi Zhang, Guang Lin*, Attention based MultiAuto-DeepONet for Operator Learning of Stochastic Problems, Journal of Computational Physics, in review.

 

[S14] Jiajun Liang, Guang Lin, Qifan Song, High-dimensional optimal mechanisms with privacy protection from uninformatic attackers, in review.

[S13] Amirhossein Mollaali, Gabriel Zufferey, Gonzalo Constante-Flores, Christian Moya, Can Li, Meng Yue, Guang Lin*, Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators, Nature Computational Science, in review.

https://arxiv.org/abs/2410.24162

[S12] Yikai Liu, Zongxin Yu, Richard J. Lindsay, Guang Lin*, Ming Chen, Abhilash Sahoo, Sonya M Hanson, ExEnDiff: An Experiment-Guided Diffusion model for protein conformational ensemble generation, PRX Life, in review. https://www.biorxiv.org/content/10.1101/2024.10.04.616517v1

[S11] Kieran Richards, Georgios Karagiannis, Guang Lin*, Likelihood free stochastic approximation Monte Carlo, Technometrics, in review.

 

[S10]        Haoyang Zheng, Guang Lin*, Quantifying patterns of uncertainty propagation via multi-fidelity Gaussian process and fuzzy sets, Journal of Computational Physics, in review.

 

[S9] Jiajun Liang, Guang Lin*, Qifan Song, Bridging differential privacy to consistent applications: A tight characterization of the worst-case risks under knowledgeable attacks, in review.

 

[S8] Gavin Ruan, Ziqi Guo, Guang Lin*, Where to build food banks and pantries: A two-level machine learning approach, Journal of Purdue Undergraduate Research, in review.

https://arxiv.org/abs/2410.15420

 

[S7] Yu Huang, Qingshan Xu, Xiaojun Lin, Guang Lin*, Robust multistage unit commitment with safe dispatch set under high renewable uncertainty, International Journal of Electrical Power and Energy Systems, in review.

 

[S6] Xing Shen, Guang Lin*, Kewei Liang, Xiaoliang Cheng, Deep Euler method for solving parametric ordinary and partial differential equations, Journal of Computational Physics, in review.

 

[S5] Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin*, Hayden Schaeffer, DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning, Computer Methods in Applied Mechanics and Engineering, in review.

https://arxiv.org/abs/2411.07239

 

[S4] Yifan Du, Wen Huang, Guang Lin*, Turbulence deconvolution using optimization on quotient manifold, Journal of Computational Physics, in review, 2022.

 

[S3]  Wentao Chen, Tehuan Chen, Guang Lin*, Reinforcement Learning for Traffic Control with Adaptive Horizon, in review. https://arxiv.org/abs/1903.12348

 

[S2]  Jiahao Zhang, Chris Moya, Guang Lin*, An Energy-based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method, Engineering Applications of Artificial Intelligence.

https://arxiv.org/html/2411.06573v1

 

[S1]  Aoxue Chen, Yifan Du, Liyao Gao, Guang Lin*, Robust data-driven discovery of partial differential equations with time-dependent coefficients, Computer Methods in Applied Mechanics and Engineering, in review.

[2102.01432] Robust data-driven discovery of partial differential equations with time-dependent coefficients (arxiv.org)

 

 

51 Fully Reviewed Conference Papers (* denotes corresponding author)

 

[C1]              Wei Deng, Yian Ma, Zhao Song, Qian Zhang, Guang Lin*, On Convergence of Federated Averaging Langevin Dynamics, 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024), Oral presentation, 2024.

https://openreview.net/forum?id=EmQGdBsOPx

 

[C2]           Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin*, Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics, accepted, ICML 2024.

      https://openreview.net/forum?id=fwyuupgAQ5

 

[C3]           Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin*, Accelerating approximate Thompson sampling with underdamped Langevin Monte Carlo, The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), May 2nd – 4th, 2024, Valencia, Spain, PMLR 238:2611-2619, 2024.

      https://proceedings.mlr.press/v238/zheng24b/zheng24b.pdf

 

[C4]           Jinwon Sohn, Guang Lin*, Qifan Song, Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes, The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), May 2nd – 4th, 2024, Valencia, Spain, PMLR 238:1594-1602, 2024.

https://proceedings.mlr.press/v238/sohn24a/sohn24a.pdf

 

[C5]           Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin*, Non-reversible Parallel Tempering for Deep Posterior Approximation, Thirty-seventh AAAI Conference on Artificial Intelligence, accepted, 2023. (Oral accepted talk)

https://ojs.aaai.org/index.php/AAAI/article/view/25893

 

[C6]           Ketian Ye, Junbo Zhao, Xiaodong Liu, Christian Moya, Guang Lin*, DeepONet Based Uncertainty Quantification for Power System Dynamics with Stochastic Loads, 2023 IEEE Power &Energy Society General Meeting (PESGM), accepted, 16-20 July 2023, Orlando, Florida, USA.

https://www.proceedings.com/content/070/070551webtoc.pdf

 

[C7]           Wenjie Li, Qifan Song, Jean Honorio, Guang Lin*, Federated X-armed bandit, 26^th The 38th Annual AAAI Conference on Artificial Intelligence, accepted, Feb 20-27, 2024, Vancouver, Canada, 38 (12), 13628-13636.

https://ojs.aaai.org/index.php/AAAI/article/view/29267

 

[C8]           Yu Huang, Dong Yue, Chunxia Dou, Guang Lin*, Outlier Detection Algorithm of Photovoltaic Power via Multivariate Dependence Modeling Based on Vine Copulas, 2022 the 6th International Conference on Power Energy Systems and Applications, Feb. 25-27, 2022, Singapore, accepted.

https://ieeexplore.ieee.org/document/9754421

 

[C9]           Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin, GLASSOFORMER: A Query-sparse transformer for post-fault power grid voltage prediction, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, 22-27 May 2022, Singapore (ICASSP 2022), accepted.

https://arxiv.org/abs/2201.09145

 

[C10]       Carson Hu, Guang Lin, Bao Wang, Meng Yue, Jack Xin, Post-Fault Power Grid Voltage Prediction via 1D-CNN with Spatial Coupling, Artificial Intelligence for Industries (ai4i 2021), accepted.

      https://www.math.uci.edu/~jxin/Powergrid_AI4I_2021.pdf

 

[C11]       Chi-Hua Wang, Wenjie Li, Guang Cheng, Guang Lin*, Federated high-dimensional online decision making, Transactions on Machine Learning Research, accepted, 2023.

     https://openreview.net/forum?id=TjaMO63fc9&noteId=nKe7G5bGkq

 

[C12]       Wei Deng, Siqi Liang, Botao Hao, Guang Lin*, Faming Liang, Interacting Contour Stochastic Gradient Langevin Dynamics, The Tenth International Conference on Learning Representations (ICLR) 2022, Virtual Meeting. (Tier 1 AI conference), Apr 25^th – 29^th, accepted.

https://openreview.net/forum?id=IK9ap6nxXr2

 

[C13]       Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin*, Faming Liang, Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction, The Ninth International Conference on Learning Representations (ICLR), May 4^th-7^th, 2021, accepted (virtual meeting). (Tier 1 AI conference)

https://openreview.net/forum?id=iOnhIy-a-0n

 

[C14]       Jaming Li, Meng Yue, Yue Zhao, Guang Lin, Machine-Learning-Based Online Transient Analysis via Iterative Computation of Generator Dynamics, 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, accepted, November 11-13, 2020, Virtual Conference.

https://par.nsf.gov/servlets/purl/10297996

 

[C15]       Wei Deng, Qi Feng, Liaoyao Gao, F. Liang, G. Lin*, Non-convex learning via replica exchange stochastic gradient MCMC, 2020 International Conference on Machine Learning (ICML), accepted, Jul 12 - 18, 2020, Virtual Meeting. (Tier 1 AI conference)

https://proceedings.mlr.press/v119/deng20b.html

 

[C16]       Wei Deng, Faming Liang, Guang Lin*, A contour stochastic gradient Langevin dynamics algorithm for simulations of multi-modal distributions, 2020 Conference on Neural Information Processing Systems (NeurIPS), Dec. 5 – Dec. 12, 2020, virtual meeting. (Tier 1 AI conference)

https://pmc.ncbi.nlm.nih.gov/articles/PMC8457681/

 

[C17]       Shiqi Zhang, Amirthagunaraj Yogarathinam, Junpeng Zhan, Meng Yue, and Guang Lin*, A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid, 2020 IEEE PES General Meeting, accepted, August 2-6, 2020, Montreal, Canada.

https://ieeexplore.ieee.org/document/9281800

 

[C18]       Xin Cai, Guang Lin*, Jinglai Li, Bayesian inverse regression for supervised dimension reduction with small datasets, the 23^rd International Conference on Artificial Intelligence and Statistics, June 3-5, 2020, Palermo, Sicily, Italy.

https://arxiv.org/abs/1906.08018

 

[C19]       Wei Deng, Junwei Pan, Tian Zhou, Aaron Eliasib Flores, Guang Lin*, DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving, 14th ACM International Web Search and Data Mining Conference, in press, Houston, TX, Feb. 19-23, 2020, online. (Tier 1 AI conference)

https://arxiv.org/pdf/2002.06987.pdf

 

[C20]       Wei Deng, Xiao Zhang, Faming Liang, Guang Lin*, An adaptive empirical Bayesian method for sparse deep learning, 2019 Conference on Neural Information Processing Systems (NeurIPS), accepted, Dec. 8 – Dec. 14, 2019, Vancouver, Canada. (Tier 1 AI conference)

https://pmc.ncbi.nlm.nih.gov/articles/PMC7687285/

 

[C21]       Liyao Gao, Hongshan Li, Zheying Lu, Guang Lin*, Rotation-equivariant convolutional neural network ensembles in image processing, Combining Physical and Data-Driven Knowledge in Ubiquitous Computing 2019 Workshop, London, UK, September 9-10, 2019.

https://doi.org/10.1145/3341162.3349330

 

[C22]       Liyao Gao, He Wang, Guang Lin*, Reflective neural network ensembles, 2019 International Joint Conference on Artificial Intelligence, August 10, 2019, Macao, P.R. China. 

 

[C23]       Yixuan Sun, Guang Lin*, Qingyou Han, Ding Yang, Corey Vian, Exploratory data analysis for achieving optimal environmental and operational parameter settings for making quality crossmember castings, Die Casting Congress & Exposition, 1, 2019.

 

[C24]       Nathan J. Keller, Andrea Vacca, Yixuan Sun, Yifei Zhou, Guang Lin*, Classification of Machine Functions: A Case Study, the 16th Scandinavian International Conference on Fluid Power, May 22-24, 2019, Tampere, Finland.

 

[C25]       Yixuan Sun, Xiaoyuan Fan, Qiuhua Huang, Xinya Li, Renke Huang, Tianzhixi Yin, Guang Lin, Local feature sufficiency exploration for predicting security-constrained generation dispatch in multi-area power systems, Special session: Machine Learning in Energy in Energy Application, 17^th IEEE International Conference on Machine Learning and Applications, Dec. 17-20, 2018, Orlando, FL, USA.

 

[C26]       J. Li, G. Lin*, Y. Huang, Decentralized dynamic power management with local information, 22nd International Conference Electronics, Palanga, Lithuania, 18th - 20th June 2018.

https://eejournal.ktu.lt/index.php/elt/article/view/22734

 

[C27]       Y. Huang, Q. Xu, X. Jiang, Y. Yang, G. Lin*, An analytic approach to probabilistic load flow incorporating correlation between non-Gaussian random variables, 22nd International Conference Electronics, Palanga, Lithuania, 18th - 20th June 2018.

https://eejournal.ktu.lt/index.php/elt/article/view/20980

 

[C28]       G. Lin and G.E. Karniadakis, A high-order discontinuous Galerkin method for modeling micro-pulsed plasma thrusters, IEPC-01-154, 27th International Electric Propulsion Conference, October 2001, Pasadena, CA.

https://www.math.purdue.edu/~lin491/pub/GLIN-01-IEPC-01-154.pdf

 

[C29]       G. Lin and G.E. Karniadakis, High-order modeling of micro-pulsed plasma thrusters, AIAA-2002-2872, 3rd AIAA Theoretical Fluid Mechanics Meeting, June 2002, St. Louis, Missouri.

 

[C30]       G. Lin, C.-H. Su and G.E. Karniadakis, Stochastic solvers for the Euler equations, AIAA-2005-0873, 43rd AIAA Aerospace Sciences Meeting and Exhibit, January 2005, Reno, NV.

 

[C31]       G. Lin, C.-H. Su and G.E. Karniadakis, Modeling random roughness in supersonic flow past a wedge, AIAA-2006-0124, 44th AIAA Aerospace Sciences Meeting and Exhibit, January 2006, Reno, NV.

 

[C32]       G. Lin, C.-H. Su and G.E. Karniadakis, Effects of Random Roughness and Scattering of Shock Waves, AIAA 2007-1134, 45th AIAA Aerospace Sciences Meeting and Exhibit, January 2007, Reno, NV.

 

[C33]       G. Lin, C.-H. Su and G.E. Karniadakis, Stochastic simulations and sensitivity analysis of plasma flow, AIAA-2008-1073, 46h AIAA Aerospace Sciences Meeting and Exhibit, January 2008, Reno, NV.

 

[C34]       Yin J, G. Lin, I Gorton, B. Han, MeDiCi-Cloud: A Workflow Infrastructure for Large-scale Scientific Applications, In 2011 Fourth IEEE International Conference on Utility and Cloud Computing (UCC), Dec. 5-8, 2011, Victoria, NSW, 336 - 337, ISBN: 978-1-4577-2116-8, 2011.

https://doi.org/10.1109/UCC.2011.56

 

[C35]       D.C. Miller, M. Syamlal, J.C. Meza, D.L. Brown, M.M. Fox, M.A. Khaleel, R.K. Cottrell, J.D. Kress, X. Sun, S. Sundaresan, N.V. Sahinidis, S.E. Zitney, D.A Agarwal, C. Tong, G. Lin, B.C. Letellier, D.W. Engel, P. Calafiura, G.A. Richards, J.H. Shinn, Overview of the U.S. DOEs Carbon Capture Simulation Initiative for Accelerating the Commercialization of CCS Technology, 36th International Technical Conference on Clean Coal & Fuel Systems, June 5 to 9, 2011, Clearwater, FL.

 

[C36]       G. Lin*, N. Zhou, T. Ferryman, and F. Tuffner, Uncertainty Quantification in State Estimation using the Probabilistic Collocation Method, Power Systems Conference and Exposition, March 20th, 2011, Phoenix, AZ.

 

[C37]       T. Ferryman, F. Tuffner, N. Zhou, and G. Lin*, Initial Study on the Predictability of Real Power on the Grid based on PMU Data, Power Systems Conference and Exposition, March 20th, 2011, Phoenix, AZ.

 

[C38]       TA Ferryman, DJ Haglin, M Vlachopoulou, J Yin, C Shen, N Zhou, G Lin, FK Tuffner, and J Tong. Net Interchange Schedule Forecasting of Electric Power Exchange for RTO/ISOs, 2012 IEEE PES General Meeting, July 22-26, 2012, San Diego, CA.

 

[C39]       D Meng, N Zhou, S Lu, and G Lin*. Estimate the Electromechanical States Using Particle Filtering and Smoothing, 2012 IEEE PES General Meeting, July 22-26, 2012, San Diego, CA.

 

[C40]       S Wang, S Lu, G Lin, and N Zhou. Measurement-based Coherency Identification and Aggregation for Power Systems, 2012 IEEE PES General Meeting, July 22-26, 2012, San Diego, CA.

 

[C41]       D. Meng, G. Lin*, M. Sushko, An Efficient Implementation of Multiscale Simulation Software PNP-cDFT, 2012 MRS Spring Meeting Proceedings, 2012, San Francisco, CA.

 

[C42]       J.B. Coble, G. Lin, B. Shumaker, P. Ramuhalli, Accurate Uncertainty Quantification to Support Online Sensor Calibration Monitoring, 2013 American Nuclear Society Winter Meeting and Technology Expo., 2013.

 

[C43]       D. Meng, N. Zhou, S. Lu, G. Lin*, An Expectation-Maximization Method for Calibrating Synchronous Machine Models, 2013 IEEE PES General Meeting, July 21-25, 2013, Vancouver, BC, Canada.

 

[C44]       J. Yin, G. Lin, Exploring Cloud Computing for Large-scale Scientific Applications, IEEE 2013 International Workshop on the Future of Software Engineering for/in the Cloud (FOSEC 2013), June 27-July 2, Santa Clara, CA.

 

[C45]       J. Bao, Z. Hou, Y. Fang, H. Ren, G. Lin, Uncertainty quantification of geomechanical responses and risk analysis of induced seismicity during geological CO2 sequestration, 12th Annual Conference on Carbon Capture, Utilization and Sequestration, Pittsburgh, PA, 2013.

 

[C46]       S.K. White, L.J. Gosink, C. Sivaramakrishnan, G.D. Black, S. Purohit, D.H. Bacon J. Hou, G. Lin, I. Gorton, A. Bonneville, Implementations of a Flexible Framework for Managing Geologic Sequestration Modeling Projects, Energy Procedia, 37: 3971–3979, 2013.

 

[C47]       Elizondo MA, S Lu, G Lin*, and S Wang, Dynamic Response of Large Wind Power Plant Affected by Diverse Conditions at Individual Turbines, In IEEE Power and Energy Society General Meeting, July 27-31, 2014, National Harbor, MD, USA.

 

[C48]       W. Li, D. Zhang, G. Lin*, A surrogate-based adaptive sampling approach for history matching and uncertainty quantification, SPE Reservoir Simulation Symposium, SPE 173298, Houston, Texas, Feb. 23-25, USA, 2015.

 

[C49]       L. Chen, Y. Cao, G. Lin, W. Sun, W. Zhang, P. Ming, Study on the simulation method of compressor aerodynamic noise based on CFD and IBEM, Proceedings of the ASME 2016 Internal Combustion Fall Technical Conference, in review, Greenville, SC, Oct. 9-12, 2016.

 

[C50]       J. Wang, X. Liu, H. Shen, G. Lin*, Multi-resolution Climate Ensemble parameter analysis with nested parallel coordinates plots, IEEE VIS Conference, Visual Analytics Science and Technology program (VAST), Oct. 23-28, 2016, Baltimore, MD, USA.

 

[C51]       A. Biswas, G. Lin, X. Liu, H. Shen, Visualization of Time-Varying Weather Ensembles Across Multiple Resolutions, IEEE VIS Conference, Scientific Visualization program (SciVis), Oct. 23-28, 2016, Baltimore, MD, USA.

 

7 Referred Formal Reports

 

[R1]  G. Lin, DW Engel, and PW Eslinger. Survey and Evaluate Uncertainty Quantification Methodologies  PNNL-20914, Pacific Northwest National Laboratory, Richland, WA, 2012. PDF

 

[R2]  CH Henager, Jr., F Gao, SY Hu, G Lin, EJ Bylaska, N Zabaras, Simulating interface growth and defect generation in CZT-simulation state of the art and known gaps, PNNL-189638, Pacific Northwest National Laboratory, Richland, WA, 2012. PDF

 

[R3]  P. Ramuhalli, G. Lin, S.L. Crawford, B. Konomi, B.G. Braatz, J.B. Coble, B. Shumaker, H. Hashemian, Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants, 2013. PDF

 

[R4]  N.A. Baker, G.E. Karniadakis, G. Lin, W. Pan, G.K. Schenter, 2013 Collaboratory on Mathematics for Mesoscopic Modeling of Materials (CM4) Annual Report, 2013. 

 

[R5]  Ramuhalli P, G. Lin, SL Crawford, BA Konomi, JB Coble, B Shumaker, and H Hashemian, Uncertainty Quantification Techniques for Sensor Calibration Monitoring in Nuclear Power Plants, 2014. PDF

 

[R1]  Winovich, Nickolas, Rushdi, Ahmad, Phipps, Eric T., Ray, Jaideep, Lin, Guang, and Ebeida, Mohamed Salah. Rigorous Data Fusion for Computationally Expensive Simulations. Sandia National Laboratory Report, SAND-2019-10322, 2019.

https://doi.org/10.2172/1560809

 

[R2]  Ben Brown, Derek DeSantis, Maria Glenski, Bhavya Kailkhura, Guang Lin, Amy McGovern, Line Pouchard, Yuhan Rao, Svitlana Volkova, Byung-Jun Yoon, AI4ESP Explainable/Interpretable/Trustworthy Artificial Intelligence Chapter for Department of Energy AI4ESP Workshop Report, 2022.

 

 

Selected 6 Keynote/plenary speeches

 

[KS1]      Plenary talk at SIAM Great Lakes Section Annual Meeting, Hammond, IN, October 12th, 2024.

 

[KS2]      College of Science’s Honorary Invited Lecture Talk, Purdue University Fort Wayne Campus, For Wayne, IN, April 25^th, 2024.

 

[KS3]      Keynote talk at Young Researcher Workshop on Uncertainty Quantification and Machine Learning, Shanghai Jiaotong University, Shanghai, China, June 5th, 2019.

 

[KS4]      Semi-keynote talk in Computational Science and Engineering in Electrochemical Energy Systems Mini-symposium at Conference on Finite Elements in Fluids, Chicago, IL, March 03/31-04/03, 2019.

 

[KS5]      Keynote Seminar at the Center for Interdisciplinary Scientific Computation, Illinois Institute of Technology, Feb. 1, 2019

 

[KS6]      Keynote talk at the second Microstructure modeling Young Researcher Forum, Southeast University, Nanjing, China, May 12, 2018.

 

Selected 187 Invited Presentations in Past Ten Years