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ORCIDGuang Lin 0001
Person information
- affiliation: Purdue University, Department of Mathematics, School of Mechanical Engineering, West Lafayette, IN, USA
Other persons with the same name
- Guang Lin — disambiguation page
- Guang Lin 0002
![[0000-0002-4948-1496]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-4948-1496")
— Tokyo University of Agriculture and Technology, Japan (and 2 more)
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2020 – today
- 2026
[j113]Haoyang Zheng, Guang Lin:
LES-SINDy: Laplace-enhanced sparse identification of nonlinear dynamical systems. J. Comput. Phys. 546: 114443 (2026)- [j112]Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer:
DeepONet as a multi-Operator extrapolation model: Distributed pretraining with physics-Informed fine-Tuning. J. Comput. Phys. 547: 114537 (2026) - [j111]Binghang Lu, Changhong Mou, Guang Lin:
iPINNER: An iterative physics-informed neural network with ensemble Kalman filter. J. Comput. Phys. 548: 114592 (2026) - 2025
- [j110]MinHyuk Jang, Jong Wook Kim, Youngdong Jang, Donghyun Kim, Wonseok Roh, Inyong Hwang, Guang Lin, Sangpil Kim:
High-quality three-dimensional cartoon avatar reconstruction with Gaussian splatting. Eng. Appl. Artif. Intell. 148: 110305 (2025) - [j109]Jiahao Zhang, Christian Moya, Guang Lin:
A self-adaptive energy-based learning rate for stochastic gradient descent via Vector Auxiliary Variable method. Eng. Appl. Artif. Intell. 160: 111731 (2025) - [j108]Jiajun Liang, Qian Zhang, Wei Deng, Qifan Song, Guang Lin:
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory. J. Comput. Graph. Stat. 34(2): 509-518 (2025) - [j107]Ziqi Guo, Daniel Carne, Krutarth Khot, Dudong Feng, Guang Lin, Xiulin Ruan:
A Review of Artificial Intelligence-Driven Approaches for Nanoscale Heat Conduction and Radiation. J. Comput. Inf. Sci. Eng. 25(12) (2025) - [j106]Binghang Lu, Zhaopeng Hao, Christian Moya, Guang Lin:
FPINN-deeponet: A physics-informed operator learning framework for multi-term time-fractional mixed diffusion-wave equations. J. Comput. Phys. 538: 114184 (2025) - [j105]Guang Lin, Changhong Mou, Jiahao Zhang:
Energy-dissipative evolutionary Kolmogorov-Arnold networks for complex PDE systems. J. Comput. Phys. 541: 114326 (2025) - [j104]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. Neural Networks 192: 107809 (2025) - [j103]Shiheng Zhang, Jiahao Zhang, Jie Shen, Guang Lin:
A Relaxed Vector Auxiliary Variable Algorithm for Unconstrained Optimization Problems. SIAM J. Sci. Comput. 47(1): 126- (2025) - [i81]Haoyang Zheng, Ruqi Zhang, Guang Lin:
Exploring Non-Convex Discrete Energy Landscapes: A Langevin-Like Sampler with Replica Exchange. CoRR abs/2501.17323 (2025) - [i80]Haoyang Zheng, Guang Lin:
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations. CoRR abs/2502.00550 (2025) - [i79]Rajdeep Haldar, Ziyi Wang, Qifan Song, Guang Lin, Yue Xing:
LLM Safety Alignment is Divergence Estimation in Disguise. CoRR abs/2502.00657 (2025) - [i78]Guang Lin, Changhong Mou, Jiahao Zhang:
Energy-Dissipative Evolutionary Kolmogorov-Arnold Networks for Complex PDE Systems. CoRR abs/2503.01618 (2025) - [i77]Cindy Xiangrui Kong, Haoyang Zheng, Guang Lin:
LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery. CoRR abs/2503.02983 (2025) - [i76]Nick Winovich, Mitchell Daneker, Lu Lu, Guang Lin:
Active operator learning with predictive uncertainty quantification for partial differential equations. CoRR abs/2503.03178 (2025) - [i75]Zecheng Zhang, Hao Liu, Wenjing Liao, Guang Lin:
Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees. CoRR abs/2503.08642 (2025) - [i74]Amirhossein Mollaali, Christian B. Moya, Amanda A. Howard, Alexander Heinlein, Panos Stinis, Guang Lin:
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning. CoRR abs/2504.15240 (2025) - [i73]Matthieu Tehenan, Christian B. Moya, Tenghai Long, Guang Lin:
Linear Spatial World Models Emerge in Large Language Models. CoRR abs/2506.02996 (2025) - [i72]Binghang Lu, Changhong Mou, Guang Lin:
An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator Learning Network. CoRR abs/2509.00663 (2025) - [i71]Haoyang Zheng, Xinyang Liu, Cindy Xiangrui Kong, Nan Jiang, Zheyuan Hu, Weijian Luo, Wei Deng, Guang Lin:
Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct. CoRR abs/2509.25035 (2025) - [i70]Weixin Wang, Haoyang Zheng, Guang Lin, Wei Deng, Pan Xu:
Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective. CoRR abs/2510.05023 (2025) - [i69]Ziyi Wang, Nan Jiang, Guang Lin, Qifan Song:
SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions. CoRR abs/2510.08999 (2025) - [i68]Zecheng Zhang, Hao Liu, Guosheng Fu, Hayden Schaeffer, Guang Lin:
Finite Element Representation Network (FERN) for Operator Learning with a Localized Trainable Basis. CoRR abs/2510.26962 (2025) - [i67]Gyeongrok Oh, Youngdong Jang, Jonghyun Choi, Suk-Ju Kang, Guang Lin, Sangpil Kim:
ICP-4D: Bridging Iterative Closest Point and LiDAR Panoptic Segmentation. CoRR abs/2512.18991 (2025) - 2024
- [j102]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. J. Comput. Phys. 496: 112600 (2024) - [j101]Jiahao Zhang, Shiheng Zhang, Jie Shen, Guang Lin:
Energy-dissipative evolutionary deep operator neural networks. J. Comput. Phys. 498: 112638 (2024) - [j100]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. J. Comput. Phys. 500: 112751 (2024) - [j99]Na Ou, Zecheng Zhang, Guang Lin:
A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method with multi-variance strategy for Bayesian inverse problems. J. Comput. Phys. 510: 113067 (2024) - [j98]Zecheng Zhang, Christian Moya, Wing Tat Leung, Guang Lin, Hayden Schaeffer:
Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE. Multiscale Model. Simul. 22(3): 956-972 (2024) - [c18]Wenjie Li, Qifan Song, Jean Honorio, Guang Lin:
Federated X-armed Bandit. AAAI 2024: 13628-13636 - [c17]Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin:
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. AISTATS 2024: 2611-2619 - [c16]Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin:
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics. ICML 2024 - [c15]Wei Deng, Qian Zhang, Yian Ma, Zhao Song, Guang Lin:
On Convergence of Federated Averaging Langevin Dynamics. UAI 2024: 1022-1054 - [i66]Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin:
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. CoRR abs/2401.11665 (2024) - [i65]Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin:
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks. CoRR abs/2402.15406 (2024) - [i64]Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin:
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics. CoRR abs/2405.07839 (2024) - [i63]Jiajun Liang, Qian Zhang, Wei Deng, Qifan Song, Guang Lin:
Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory. CoRR abs/2407.06935 (2024) - [i62]Rajdeep Haldar, Yue Xing, Qifan Song, Guang Lin:
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility. CoRR abs/2410.06921 (2024) - [i61]Gavin Ruan, Ziqi Guo, Guang Lin:
Where to Build Food Banks and Pantries: A Two-Level Machine Learning Approach. CoRR abs/2410.15420 (2024) - [i60]Amirhossein Mollaali, Gabriel Zufferey, Gonzalo Constante-Flores, Christian Moya, Can Li, Guang Lin, Meng Yue:
Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators. CoRR abs/2410.24162 (2024) - [i59]Haoyang Zheng, Guang Lin:
LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems. CoRR abs/2411.01719 (2024) - [i58]Jiahao Zhang, Christian Moya, Guang Lin:
An Energy-Based Self-Adaptive Learning Rate for Stochastic Gradient Descent: Enhancing Unconstrained Optimization with VAV method. CoRR abs/2411.06573 (2024) - [i57]Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer:
DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning. CoRR abs/2411.07239 (2024) - [i56]Dustin Enyeart, Guang Lin:
Loss Terms and Operator Forms of Koopman Autoencoders. CoRR abs/2412.04578 (2024) - [i55]Dustin Enyeart, Guang Lin:
Some Best Practices in Operator Learning. CoRR abs/2412.06686 (2024) - [i54]Dustin Enyeart, Guang Lin:
Adversarial Autoencoders in Operator Learning. CoRR abs/2412.07811 (2024) - 2023
- [j97]Binghang Lu, Christian Moya, Guang Lin:
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training. Algorithms 16(4): 194 (2023) - [j96]Guang Lin, Christian Moya, Zecheng Zhang:
Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks. Eng. Appl. Artif. Intell. 125: 106689 (2023) - [j95]Christian Moya, Shiqi Zhang, Guang Lin, Meng Yue:
DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid's post-fault trajectories. Neurocomputing 535: 166-182 (2023) - [j94]Yan Xiang, Yu-Hang Tang, Guang Lin, Daniel Reker:
Interpretable Molecular Property Predictions Using Marginalized Graph Kernels. J. Chem. Inf. Model. 63(15): 4633-4640 (2023) - [j93]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. J. Chem. Inf. Model. 63(21): 6515-6524 (2023) - [j92]Christian Moya, Guang Lin:
Bayesian, Multifidelity Operator Learning for Complex Engineering Systems-A Position Paper. J. Comput. Inf. Sci. Eng. 23(6) (2023) - [j91]Guang Lin, Christian Moya, Zecheng Zhang:
B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD. J. Comput. Phys. 473: 111713 (2023) - [j90]Christian Moya, Guang Lin:
DAE-PINN: a physics-informed neural network model for simulating differential algebraic equations with application to power networks. Neural Comput. Appl. 35(5): 3789-3804 (2023) - [j89]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 Syst. J. 17(3): 4360-4370 (2023) - [j88]Xinchao Liu, Xiao Liu, Tulin Kaman, Xiaohua Lu, Guang Lin:
Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions. Technometrics 65(4): 564-578 (2023) - [c14]Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin:
Non-reversible Parallel Tempering for Deep Posterior Approximation. AAAI 2023: 7332-7339 - [i53]Guanxun Li, Guang Lin, Zecheng Zhang, Quan Zhou:
Fast Replica Exchange Stochastic Gradient Langevin Dynamics. CoRR abs/2301.01898 (2023) - [i52]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. CoRR abs/2301.12538 (2023) - [i51]Binghang Lu, Christian B. Moya, Guang Lin:
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training. CoRR abs/2303.02219 (2023) - [i50]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. CoRR abs/2304.02811 (2023) - [i49]Guihong Wang, Yuqing Li, Tao Luo, Zheng Ma, Nung Kwan Yip, Guang Lin:
Numerical Stability for Differential Equations with Memory. CoRR abs/2305.06571 (2023) - [i48]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. CoRR abs/2306.00810 (2023) - [i47]Jiahao Zhang, Shiheng Zhang, Jie Shen, Guang Lin:
Energy-Dissipative Evolutionary Deep Operator Neural Networks. CoRR abs/2306.06281 (2023) - [i46]Zecheng Zhang, Christian Moya, Wing Tat Leung, Guang Lin, Hayden Schaeffer:
Bayesian deep operator learning for homogenized to fine-scale maps for multiscale PDE. CoRR abs/2308.14188 (2023) - [i45]Shiheng Zhang, Jiahao Zhang, Jie Shen, Guang Lin:
An Element-wise RSAV Algorithm for Unconstrained Optimization Problems. CoRR abs/2309.04013 (2023) - [i44]Guang Lin, Na Ou, Zecheng Zhang, Zhidong Zhang:
Restoring the Discontinuous Heat Equation Source Using Sparse Boundary Data and Dynamic Sensors. CoRR abs/2310.01541 (2023) - [i43]Yikai Liu, Ming Chen, Guang Lin:
Backdiff: a diffusion model for generalized transferable protein backmapping. CoRR abs/2310.01768 (2023) - [i42]Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer:
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators. CoRR abs/2310.18888 (2023) - [i41]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. CoRR abs/2311.03639 (2023) - [i40]Jinwon Sohn, Qifan Song, Guang Lin:
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes. CoRR abs/2311.05866 (2023) - [i39]Zhihao Kong, Amirhossein Mollaali, Christian Moya, Na Lu, Guang Lin:
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions. CoRR abs/2311.16519 (2023) - [i38]Yikai Liu, Tushar K. Ghosh, Guang Lin, Ming Chen:
Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model. CoRR abs/2312.09404 (2023) - 2022
- [j87]Christian Moya, Guang Lin:
Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks. Algorithms 15(9): 325 (2022) - [j86]Yao Huang, Wenrui Hao, Guang Lin:
HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations. Comput. Math. Appl. 121: 62-73 (2022) - [j85]Georgios Karagiannis, Zhangshuan Hou, Maoyi Huang, Guang Lin:
Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework. Comput. 10(5): 72 (2022) - [j84]Shruthi Suresh, David T. Newton, Thomas H. Everett, Guang Lin, Bradley S. Duerstock:
Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia. Frontiers Neuroinformatics 16 (2022) - [j83]Suman Chakraborty, Yixuan Sun, Guang Lin, Li Qiao:
Vapor-liquid equilibrium estimation of n-alkane/nitrogen mixtures using neural networks. J. Comput. Appl. Math. 408: 114059 (2022) - [j82]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
A consistent and conservative Phase-Field method for multiphase incompressible flows. J. Comput. Appl. Math. 408: 114116 (2022) - [j81]Dongwu Wang, Bin Zheng, Long Chen, Guang Lin, Jinchao Xu:
Block triangular preconditioning for stochastic Galerkin method. J. Comput. Appl. Math. 412: 114298 (2022) - [j80]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
A consistent and conservative Phase-Field model for thermo-gas-liquid-solid flows including liquid-solid phase change. J. Comput. Phys. 449: 110795 (2022) - [j79]Guang Lin, Yating Wang, Zecheng Zhang:
Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN. J. Comput. Phys. 460: 111173 (2022) - [j78]Liyao Gao, Yifan Du, Hongshan Li, Guang Lin:
RotEqNet: Rotation-equivariant network for fluid systems with symmetric high-order tensors. J. Comput. Phys. 461: 111205 (2022) - [j77]Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang:
Efficient hybrid explicit-implicit learning for multiscale problems. J. Comput. Phys. 467: 111326 (2022) - [j76]Hugo Esquivel, Arun Prakash, Guang Lin:
Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems. J. Comput. Phys. 467: 111425 (2022) - [j75]Wing Tat Leung, Guang Lin, Zecheng Zhang:
NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems. J. Comput. Phys. 470: 111539 (2022) - [j74]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows. J. Comput. Phys. 471: 111619 (2022) - [j73]Yating Wang, Wing Tat Leung, Guang Lin:
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems. Multiscale Model. Simul. 20(1): 618-640 (2022) - [j72]Haoyang Zheng, Jeffrey R. Petrella, P. Murali Doraiswamy, Guang Lin, Wenrui Hao:
Data-driven causal model discovery and personalized prediction in Alzheimer's disease. npj Digit. Medicine 5 (2022) - [j71]Wei Deng, Guang Lin, Faming Liang:
An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization. Stat. Comput. 32(4): 58 (2022) - [j70]Tehuan Chen, Zhigang Ren, Guang Lin, Chao Xu:
Learning-PDE-Based Approximate Optimal Control for an MHD System With Uncertainty Quantification. IEEE Trans. Syst. Man Cybern. Syst. 52(11): 7185-7192 (2022) - [c13]Carson Hu, Guang Lin, Bao Wang, Meng Yue, Jack Xin:
Post-Fault Power Grid Voltage Prediction via 1D-CNN with Spatial Coupling. AI4I 2022: 35-37 - [c12]Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin:
Glassoformer: A Query-Sparse Transformer for Post-Fault Power Grid Voltage Prediction. ICASSP 2022: 3968-3972 - [c11]Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang:
Interacting Contour Stochastic Gradient Langevin Dynamics. ICLR 2022 - [i37]Yunling Zheng, Carson Hu, Guang Lin, Meng Yue, Bao Wang, Jack Xin:
glassoformer: a query-sparse transformer for post-fault power grid voltage prediction. CoRR abs/2201.09145 (2022) - [i36]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. CoRR abs/2202.07176 (2022) - [i35]Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang:
Interacting Contour Stochastic Gradient Langevin Dynamics. CoRR abs/2202.09867 (2022) - [i34]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. CoRR abs/2204.02583 (2022) - [i33]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. CoRR abs/2204.03193 (2022) - [i32]Jiahao Zhang, Shiqi Zhang, Guang Lin:
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification. CoRR abs/2204.04819 (2022) - [i31]Sheng Zhang, Guang Lin, Samy Tindel:
2-d signature of images and texture classification. CoRR abs/2205.11236 (2022) - [i30]Wenjie Li, Qifan Song, Jean Honorio, Guang Lin:
Federated X-Armed Bandit. CoRR abs/2205.15268 (2022) - [i29]Guang Lin, Christian Moya, Zecheng Zhang:
On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks. CoRR abs/2206.06536 (2022) - [i28]Hugo Esquivel, Arun Prakash, Guang Lin:
Multi-element flow-driven spectral chaos (ME-FSC) method for uncertainty quantification of dynamical systems. CoRR abs/2207.10281 (2022) - [i27]Yating Wang, Wing Tat Leung, Guang Lin:
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems. CoRR abs/2207.11735 (2022) - [i26]Yan Xiang, Yu-Hang Tang, Zheng Gong, Hongyi Liu, Liang Wu, Guang Lin, Huai Sun:
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation. CoRR abs/2209.00514 (2022) - [i25]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. CoRR abs/2209.10622 (2022) - [i24]Na Ou, Zecheng Zhang, Guang Lin:
A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method for Bayesian inverse problems. CoRR abs/2210.17048 (2022) - [i23]Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin:
Non-reversible Parallel Tempering for Deep Posterior Approximation. CoRR abs/2211.10837 (2022) - 2021
- [j69]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) - [j68]Jun Man, Guang Lin, Yijun Yao, Lingzao Zeng:
A generalized multi-fidelity simulation method using sparse polynomial chaos expansion. J. Comput. Appl. Math. 397: 113613 (2021) - [j67]Hugo Esquivel, Arun Prakash, Guang Lin:
Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems. J. Comput. Appl. Math. 398: 113674 (2021) - [j66]Yan Xiang, Yu-Hang Tang, Guang Lin, Huai Sun:
A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network. J. Chem. Inf. Model. 61(11): 5414-5424 (2021) - [j65]Sheng Zhang, Guang Lin:
SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations. J. Comput. Phys. 428: 109962 (2021) - [j64]Yuepeng Wang, Xuemei Ding, Kun Hu, Fangxin Fang, I. M. Navon, Guang Lin:
Feasibility of DEIM for retrieving the initial field via dimensionality reduction. J. Comput. Phys. 429: 110005 (2021) - [j63]Hugo Esquivel, Arun Prakash, Guang Lin:
Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems. J. Comput. Phys. 430: 110044 (2021) - [j62]Yating Wang, Wei Deng, Guang Lin:
Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications. J. Comput. Phys. 432: 110134 (2021) - [j61]Yating Wang, Wei Deng, Guang Lin:
An adaptive Hessian approximated stochastic gradient MCMC method. J. Comput. Phys. 432: 110150 (2021) - [j60]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
A consistent and conservative model and its scheme for N-phase-M-component incompressible flows. J. Comput. Phys. 434: 110229 (2021) - [j59]Ehsan Kharazmi, Min Cai, Xiaoning Zheng, Zhen Zhang, Guang Lin, George Em Karniadakis:
Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks. Nat. Comput. Sci. 1(11): 744-753 (2021) - [j58]Sheng Zhang, Joan Ponce, Zhen Zhang, Guang Lin, George E. Karniadakis:
An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City. PLoS Comput. Biol. 17(9) (2021) - [j57]Jiuhai Chen, Lulu Kang, Guang Lin:
Gaussian Process Assisted Active Learning of Physical Laws. Technometrics 63(3): 329-342 (2021) - [c10]Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang:
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. ICLR 2021 - [c9]Wei Deng, Junwei Pan, Tian Zhou, Deguang Kong, Aaron Flores, Guang Lin:
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. WSDM 2021: 922-930 - [i22]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
A consistent and conservative model and its scheme for N-phase-M-component incompressible flows. CoRR abs/2101.04252 (2021) - [i21]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
A consistent and conservative Phase-Field model for thermo-gas-liquid-solid flows including liquid-solid phase change. CoRR abs/2102.06863 (2021) - [i20]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows. CoRR abs/2103.07839 (2021) - [i19]Hugo Esquivel, Arun Prakash, Guang Lin:
Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems. CoRR abs/2105.10544 (2021) - [i18]Guang Lin, Yating Wang, Zecheng Zhang:
Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network. CoRR abs/2107.06330 (2021) - [i17]Wing Tat Leung, Guang Lin, Zecheng Zhang:
NH-PINN: Neural homogenization based physics-informed neural network for multiscale problems. CoRR abs/2108.12942 (2021) - [i16]Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang:
HEI: hybrid explicit-implicit learning for multiscale problems. CoRR abs/2109.02147 (2021) - [i15]Christian Moya, Guang Lin:
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks. CoRR abs/2109.04304 (2021) - [i14]Haoyang Zheng, Ziyang Huang, Guang Lin:
PCNN: A physics-constrained neural network for multiphase flows. CoRR abs/2109.08965 (2021) - [i13]Guang Lin, Zecheng Zhang, Zhidong Zhang:
Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements. CoRR abs/2111.02285 (2021) - [i12]Guang Lin, Christian Moya, Zecheng Zhang:
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs. CoRR abs/2111.02484 (2021) - [i11]Wei Deng, Yi-An Ma, Zhao Song, Qian Zhang, Guang Lin:
On Convergence of Federated Averaging Langevin Dynamics. CoRR abs/2112.05120 (2021) - 2020
- [j56]Zhaopeng Hao, Guang Lin, Zhongqiang Zhang:
Error estimates of a spectral Petrov-Galerkin method for two-sided fractional reaction-diffusion equations. Appl. Math. Comput. 374: 125045 (2020) - [j55]Yiqi Gu, Xi Yang, Mengjiao Peng, Guang Lin:
Robust weighted SVD-type latent factor models for rating prediction. Expert Syst. Appl. 141 (2020) - [j54]Yating Wang, Guang Lin:
Efficient deep learning techniques for multiphase flow simulation in heterogeneous porousc media. J. Comput. Phys. 401 (2020) - [j53]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
Consistent, essentially conservative and balanced-force Phase-Field method to model incompressible two-phase flows. J. Comput. Phys. 406: 109192 (2020) - [j52]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
Consistent and conservative scheme for incompressible two-phase flows using the conservative Allen-Cahn model. J. Comput. Phys. 420: 109718 (2020) - [j51]Tehuan Chen, Zhigang Ren, Guang Lin, Zongze Wu, Bao-Lin Ye:
Real-time computational optimal control of an MHD flow system with parameter uncertainty quantification. J. Frankl. Inst. 357(5): 2830-2850 (2020) - [j50]Wenrui Hao, Jan S. Hesthaven, Guang Lin, Bin Zheng:
A Homotopy Method with Adaptive Basis Selection for Computing Multiple Solutions of Differential Equations. J. Sci. Comput. 82(1): 19 (2020) - [j49]Na Ou, Guang Lin, Lijian Jiang:
A Low-Rank Approximated Multiscale Method for Pdes With Random Coefficients. Multiscale Model. Simul. 18(4): 1595-1620 (2020) - [j48]Sangpil Kim, Nick Winovich, Hyung-Gun Chi, Guang Lin, Karthik Ramani:
Latent transformations neural network for object view synthesis. Vis. Comput. 36(8): 1663-1677 (2020) - [c8]Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin:
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. ICML 2020: 2474-2483 - [c7]Wei Deng, Guang Lin, Faming Liang:
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. NeurIPS 2020 - [c6]Jiaming Li, Meng Yue, Yue Zhao, Guang Lin:
Machine-Learning-Based Online Transient Analysis via Iterative Computation of Generator Dynamics. SmartGridComm 2020: 1-6 - [i10]Wei Deng, Junwei Pan, Tian Zhou, Aaron Flores, Guang Lin:
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. CoRR abs/2002.06987 (2020) - [i9]Yating Wang, Wei Deng, Guang Lin:
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications. CoRR abs/2006.16376 (2020) - [i8]Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin:
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. CoRR abs/2008.05367 (2020) - [i7]Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang:
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. CoRR abs/2010.01084 (2020) - [i6]Yating Wang, Wei Deng, Guang Lin:
An adaptive Hessian approximated stochastic gradient MCMC method. CoRR abs/2010.01384 (2020) - [i5]Wei Deng, Guang Lin, Faming Liang:
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. CoRR abs/2010.09800 (2020) - [i4]Hugo Esquivel, Arun Prakash, Guang Lin:
Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems. CoRR abs/2012.01496 (2020)
2010 – 2019
- 2019
- [j47]Yu Huang, Qingshan Xu, Sajjad Abedi, Tong Zhang, Xianqiang Jiang, Guang Lin:
Stochastic Security Assessment for Power Systems With High Renewable Energy Penetration Considering Frequency Regulation. IEEE Access 7: 6450-6460 (2019) - [j46]Jun Yang, Wei Wang, Guang Lin, Qing Li, Yeqing Sun, Yixuan Sun:
Infrared Thermal Imaging-Based Crack Detection Using Deep Learning. IEEE Access 7: 182060-182077 (2019) - [j45]Yuepeng Wang, Kun Hu, Lanlan Ren, Guang Lin:
Optimal observations-based retrieval of topography in 2D shallow water equations using PC-EnKF. J. Comput. Phys. 382: 43-60 (2019) - [j44]Ziyang Huang, Guang Lin, Arezoo Motavalizadeh Ardekani:
A mixed upwind/central WENO scheme for incompressible two-phase flows. J. Comput. Phys. 387: 455-480 (2019) - [j43]Nick Winovich, Karthik Ramani, Guang Lin:
ConvPDE-UQ: Convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains. J. Comput. Phys. 394: 263-279 (2019) - [j42]Zhaopeng Hao, Moongyu Park, Guang Lin, Zhiqiang Cai:
Finite Element Method for Two-Sided Fractional Differential Equations with Variable Coefficients: Galerkin Approach. J. Sci. Comput. 79(2): 700-717 (2019) - [c5]Liyao Gao, Hongshan Li, Zheying Lu, Guang Lin:
Rotation-equivariant convolutional neural network ensembles in image processing. UbiComp/ISWC Adjunct 2019: 551-557 - [c4]Wei Deng, Xiao Zhang, Faming Liang, Guang Lin:
An Adaptive Empirical Bayesian Method for Sparse Deep Learning. NeurIPS 2019: 5564-5574 - [i3]Wei Deng, Xiao Zhang, Faming Liang, Guang Lin:
An Adaptive Empirical Bayesian Method for Sparse Deep Learning. CoRR abs/1910.10791 (2019) - 2018
- [j41]Mu Wang, Guang Lin, Alex Pothen:
Using automatic differentiation for compressive sensing in uncertainty quantification. Optim. Methods Softw. 33(4-6): 799-812 (2018) - [j40]Yingwei Wang, Wenrui Hao, Guang Lin:
Two-Level Spectral Methods for Nonlinear Elliptic Equations with Multiple Solutions. SIAM J. Sci. Comput. 40(4): B1180-B1205 (2018) - [c3]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. ICMLA 2018: 1283-1289 - [i2]Sangpil Kim, Nick Winovich, Guang Lin, Karthik Ramani:
CT-GAN: Conditional Transformation Generative Adversarial Network for Image Attribute Modification. CoRR abs/1807.04812 (2018) - 2017
- [j39]Zhao-peng Hao, Guang Lin, Zhi-Zhong Sun:
A high-order difference scheme for the fractional sub-diffusion equation. Int. J. Comput. Math. 94(2): 405-426 (2017) - [j38]Zhaopeng Hao, Wanrong Cao, Guang Lin:
A second-order difference scheme for the time fractional substantial diffusion equation. J. Comput. Appl. Math. 313: 54-69 (2017) - [j37]Luoping Chen, Bin Zheng, Guang Lin, Nikolaos K. Voulgarakis:
A two-level stochastic collocation method for semilinear elliptic equations with random coefficients. J. Comput. Appl. Math. 315: 195-207 (2017) - [j36]Georgios Karagiannis, Guang Lin:
On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models. J. Comput. Phys. 342: 139-160 (2017) - [j35]Georgios Karagiannis, Bledar A. Konomi, Guang Lin, Faming Liang:
Parallel and interacting stochastic approximation annealing algorithms for global optimisation. Stat. Comput. 27(4): 927-945 (2017) - [j34]Emilie Purvine, Eduardo Cotilla Sanchez, Mahantesh Halappanavar, Zhenyu Huang, Guang Lin, Shuai Lu, Shaobu Wang:
Comparative study of clustering techniques for real-time dynamic model reduction. Stat. Anal. Data Min. 10(5): 263-276 (2017) - [j33]Junpeng Wang, Xiaotong Liu, Han-Wei Shen, Guang Lin:
Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots. IEEE Trans. Vis. Comput. Graph. 23(1): 81-90 (2017) - [j32]Ayan Biswas, Guang Lin, Xiaotong Liu, Han-Wei Shen:
Visualization of Time-Varying Weather Ensembles across Multiple Resolutions. IEEE Trans. Vis. Comput. Graph. 23(1): 841-850 (2017) - 2016
- [j31]Yuzhou Sun, Pengtao Sun, Bin Zheng, Guang Lin:
Error analysis of finite element method for Poisson-Nernst-Planck equations. J. Comput. Appl. Math. 301: 28-43 (2016) - [j30]Xiu Yang, Huan Lei, Nathan A. Baker, Guang Lin:
Enhancing sparsity of Hermite polynomial expansions by iterative rotations. J. Comput. Phys. 307: 94-109 (2016) - [j29]Hongqiao Wang, Guang Lin, Jinglai Li:
Gaussian process surrogates for failure detection: A Bayesian experimental design approach. J. Comput. Phys. 313: 247-259 (2016) - [j28]Qifeng Liao, Guang Lin:
Reduced basis ANOVA methods for partial differential equations with high-dimensional random inputs. J. Comput. Phys. 317: 148-164 (2016) - [j27]Weixuan Li, Guang Lin, Bing Li:
Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice. J. Comput. Phys. 321: 259-278 (2016) - [j26]Victor Ginting, Guang Lin, Jiangguo Liu:
On Application of the Weak Galerkin Finite Element Method to a Two-Phase Model for Subsurface Flow. J. Sci. Comput. 66(1): 225-239 (2016) - [j25]Ido Bright, Guang Lin, J. Nathan Kutz:
Classification of Spatiotemporal Data via Asynchronous Sparse Sampling: Application to Flow around a Cylinder. Multiscale Model. Simul. 14(2): 823-838 (2016) - [j24]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 Comput. Appl. 27(2): 447-457 (2016) - 2015
- [j23]Guang Lin, Jiangguo Liu, Farrah Sadre-Marandi:
A comparative study on the weak Galerkin, discontinuous Galerkin, and mixed finite element methods. J. Comput. Appl. Math. 273: 346-362 (2015) - [j22]Wenrui Hao, Zhiliang Xu, Chun Liu, Guang Lin:
A fictitious domain method with a hybrid cell model for simulating motion of cells in fluid flow. J. Comput. Phys. 280: 345-362 (2015) - [j21]Georgios Karagiannis, Bledar A. Konomi, Guang Lin:
A Bayesian mixed shrinkage prior procedure for spatial-stochastic basis selection and evaluation of gPC expansions: Applications to elliptic SPDEs. J. Comput. Phys. 284: 528-546 (2015) - [j20]Weixuan Li, Guang Lin:
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions. J. Comput. Phys. 294: 173-190 (2015) - [j19]Jinglai Li, Guang Lin, Xu Yang:
A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion. J. Comput. Phys. 296: 58-71 (2015) - [j18]Bohai Zhang, Bledar A. Konomi, Huiyan Sang, Georgios Karagiannis, Guang Lin:
Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. J. Comput. Phys. 300: 623-642 (2015) - [j17]Huan Lei, Xiu Yang, Bin Zheng, Guang Lin, Nathan A. Baker:
Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the Influence of Conformational Uncertainty in Biomolecular Solvation. Multiscale Model. Simul. 13(4): 1327-1353 (2015) - [i1]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. CoRR abs/1501.00943 (2015) - 2014
- [j16]Weixuan Li, Guang Lin, Dongxiao Zhang:
An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling. J. Comput. Phys. 258: 752-772 (2014) - [j15]Georgios Karagiannis, Guang Lin:
Selection of polynomial chaos bases via Bayesian model uncertainty methods with applications to sparse approximation of PDEs with stochastic inputs. J. Comput. Phys. 259: 114-134 (2014) - [j14]Guang Lin, Jiangguo Liu, Lin Mu, Xiu Ye:
Weak Galerkin finite element methods for Darcy flow: Anisotropy and heterogeneity. J. Comput. Phys. 276: 422-437 (2014) - [j13]Gongjun Xu, Guang Lin, Jingchen Liu:
Rare-Event Simulation for the Stochastic Korteweg-de Vries Equation. SIAM/ASA J. Uncertain. Quantification 2(1): 698-716 (2014) - [j12]Bledar A. Konomi, Georgios Karagiannis, Avik Sarkar, Xin Sun, Guang Lin:
Bayesian Treed Multivariate Gaussian Process With Adaptive Design: Application to a Carbon Capture Unit. Technometrics 56(2): 145-158 (2014) - 2013
- [j11]Jie Bao, Zhijie Xu, Guang Lin, Yilin Fang:
Evaluating the impact of aquifer layer properties on geomechanical response during CO2 geological sequestration. Comput. Geosci. 54: 28-37 (2013) - [j10]Zhongqiang Zhang, Xiu Yang, Guang Lin, George E. Karniadakis:
Numerical solution of the Stratonovich- and Ito-Euler equations: Application to the stochastic piston problem. J. Comput. Phys. 236: 15-27 (2013) - [j9]Ilias Bilionis, Nicholas Zabaras, Bledar A. Konomi, Guang Lin:
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification. J. Comput. Phys. 241: 212-239 (2013) - [j8]Xiaoliang Wan, Guang Lin:
Hybrid parallel computing of minimum action method. Parallel Comput. 39(10): 638-651 (2013) - [c2]Guang Lin, Binh Han, Jian Yin, Ian Gorton:
Exploring Cloud Computing for Large-Scale Scientific Applications. SERVICES 2013: 37-43 - 2012
- [j7]Xiu Yang, Minseok Choi, Guang Lin, George E. Karniadakis:
Adaptive ANOVA decomposition of stochastic incompressible and compressible flows. J. Comput. Phys. 231(4): 1587-1614 (2012) - 2011
- [j6]Zhiliang Xu, Yingjie Liu, Huijing Du, Guang Lin, Chi-Wang Shu:
Point-wise hierarchical reconstruction for discontinuous Galerkin and finite volume methods for solving conservation laws. J. Comput. Phys. 230(17): 6843-6865 (2011) - [c1]Jian Yin, Guang Lin, Ian Gorton, Binh Han:
MeDiCi-Cloud: A Workflow Infrastructure for Large-scale Scientific Applications. UCC 2011: 336-337 - 2010
- [j5]Guang Lin, Alexandre M. Tartakovsky, Daniel M. Tartakovsky:
Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids. J. Comput. Phys. 229(19): 6995-7012 (2010) - [j4]Guang Lin, Alexandre M. Tartakovsky:
Numerical Studies of Three-dimensional Stochastic Darcy's Equation and Stochastic Advection-Diffusion-Dispersion Equation. J. Sci. Comput. 43(1): 92-117 (2010)
2000 – 2009
- 2007
- [j3]Guang Lin, Xiaoliang Wan, Chau-Hsing Su, George E. Karniadakis:
Stochastic Computational Fluid Mechanics. Comput. Sci. Eng. 9(2): 21-29 (2007) - 2006
- [j2]Guang Lin, Leopold Grinberg, George E. Karniadakis:
Numerical studies of the stochastic Korteweg-de Vries equation. J. Comput. Phys. 213(2): 676-703 (2006) - [j1]Guang Lin, Chau-Hsing Su, George E. Karniadakis:
Predicting shock dynamics in the presence of uncertainties. J. Comput. Phys. 217(1): 260-276 (2006)
Coauthor Index
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