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2020 – today
- 2024
[j104]Yuexiang Li
, Yanping Wang, Guang Lin, Yawen Huang, Jingxin Liu, Yi Lin, Dong Wei, Qirui Zhang, Kai Ma, Zhiqiang Zhang, Guangming Lu, Yefeng Zheng:
Triplet-branch network with contrastive prior-knowledge embedding for disease grading. Artif. Intell. Medicine 149: 102801 (2024)- [j103]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) - [j102]Jiahao Zhang, Shiheng Zhang, Jie Shen, Guang Lin:
Energy-dissipative evolutionary deep operator neural networks. J. Comput. Phys. 498: 112638 (2024) - [j101]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) - [j100]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) - [c25]Wenjie Li, Qifan Song, Jean Honorio, Guang Lin:
Federated X-armed Bandit. AAAI 2024: 13628-13636 - [c24]Jinwon Sohn, Qifan Song, Guang Lin:
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes. AISTATS 2024: 1594-1602 - [c23]Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin:
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. AISTATS 2024: 2611-2619 - [c22]Guang Lin, Chao Li, Jianhai Zhang, Toshihisa Tanaka, Qibin Zhao:
Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization. ICLR 2024 - [c21]Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin:
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics. ICML 2024 - [i73]Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin:
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. CoRR abs/2401.11665 (2024) - [i72]Guang Lin, Chao Li, Jianhai Zhang, Toshihisa Tanaka, Qibin Zhao:
Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization. CoRR abs/2401.16352 (2024) - [i71]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) - [i70]Guang Lin, Zerui Tao, Jianhai Zhang, Toshihisa Tanaka, Qibin Zhao:
Robust Diffusion Models for Adversarial Purification. CoRR abs/2403.16067 (2024) - [i69]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) - [i68]Guang Lin, Qibin Zhao:
Large Language Model Sentinel: Advancing Adversarial Robustness by LLM Agent. CoRR abs/2405.20770 (2024) - [i67]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) - 2023
- [j99]Binghang Lu, Christian Moya, Guang Lin:
NSGA-PINN: A Multi-Objective Optimization Method for Physics-Informed Neural Network Training. Algorithms 16(4): 194 (2023) - [j98]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) - [j97]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) - [j96]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) - [j95]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) - [j94]Christian Moya, Guang Lin:
Bayesian, Multifidelity Operator Learning for Complex Engineering Systems-A Position Paper. J. Comput. Inf. Sci. Eng. 23(6) (2023) - [j93]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) - [j92]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) - [j91]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) - [j90]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) - [j89]Chi-Hua Wang, Wenjie Li, Guang Lin:
Federated High-Dimensional Online Decision Making. Trans. Mach. Learn. Res. 2023 (2023) - [c20]Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin:
Non-reversible Parallel Tempering for Deep Posterior Approximation. AAAI 2023: 7332-7339 - [i66]Guanxun Li, Guang Lin, Zecheng Zhang, Quan Zhou:
Fast Replica Exchange Stochastic Gradient Langevin Dynamics. CoRR abs/2301.01898 (2023) - [i65]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) - [i64]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) - [i63]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) - [i62]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) - [i61]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) - [i60]Jiahao Zhang, Shiheng Zhang, Jie Shen, Guang Lin:
Energy-Dissipative Evolutionary Deep Operator Neural Networks. CoRR abs/2306.06281 (2023) - [i59]Guang Lin, Jianhai Zhang:
Multi-Subdomain Adversarial Network for Cross-Subject EEG-based Emotion Recognition. CoRR abs/2308.14059 (2023) - [i58]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) - [i57]Shiheng Zhang, Jiahao Zhang, Jie Shen, Guang Lin:
An Element-wise RSAV Algorithm for Unconstrained Optimization Problems. CoRR abs/2309.04013 (2023) - [i56]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) - [i55]Yikai Liu, Ming Chen, Guang Lin:
Backdiff: a diffusion model for generalized transferable protein backmapping. CoRR abs/2310.01768 (2023) - [i54]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) - [i53]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) - [i52]Jinwon Sohn, Qifan Song, Guang Lin:
Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes. CoRR abs/2311.05866 (2023) - [i51]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) - [i50]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
- [j88]Christian Moya, Guang Lin:
Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks. Algorithms 15(9): 325 (2022) - [j87]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) - [j86]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) - [j85]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) - [j84]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) - [j83]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) - [j82]Dongwu Wang, Bin Zheng, Long Chen, Guang Lin, Jinchao Xu:
Block triangular preconditioning for stochastic Galerkin method. J. Comput. Appl. Math. 412: 114298 (2022) - [j81]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) - [j80]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) - [j79]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) - [j78]Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang:
Efficient hybrid explicit-implicit learning for multiscale problems. J. Comput. Phys. 467: 111326 (2022) - [j77]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) - [j76]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) - [j75]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) - [j74]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) - [j73]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) - [j72]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) - [j71]Liyao Gao, Guang Lin, Wei Zhu:
Deformation Robust Roto-Scale-Translation Equivariant CNNs. Trans. Mach. Learn. Res. 2022 (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) - [c19]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 - [c18]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 - [c17]Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang:
Interacting Contour Stochastic Gradient Langevin Dynamics. ICLR 2022 - [i49]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) - [i48]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) - [i47]Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang:
Interacting Contour Stochastic Gradient Langevin Dynamics. CoRR abs/2202.09867 (2022) - [i46]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) - [i45]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) - [i44]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) - [i43]Sheng Zhang, Guang Lin, Samy Tindel:
2-d signature of images and texture classification. CoRR abs/2205.11236 (2022) - [i42]Wenjie Li, Qifan Song, Jean Honorio, Guang Lin:
Federated X-Armed Bandit. CoRR abs/2205.15268 (2022) - [i41]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) - [i40]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) - [i39]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) - [i38]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) - [i37]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) - [i36]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]Hui Huang, Jianhai Zhang, Li Zhu, Jiajia Tang, Guang Lin, Wanzeng Kong, Xu Lei, Lei Zhu:
EEG-Based Sleep Staging Analysis with Functional Connectivity. Sensors 21(6): 1988 (2021) - [j56]Jiuhai Chen, Lulu Kang, Guang Lin:
Gaussian Process Assisted Active Learning of Physical Laws. Technometrics 63(3): 329-342 (2021) - [c16]Guang Lin, Li Zhu, Bin Ren, Yiteng Hu, Jianhai Zhang:
Multi-Branch Network for Cross-Subject EEG-based Emotion Recognition. ACML 2021: 705-720 - [c15]Lang Hu, Li Zhu, Hui Huang, Guang Lin, Bin Ren, Jianhai Zhang:
Speaker recognition with voice evoked EEG. BIBM 2021: 2231-2236 - [c14]Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang:
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. ICLR 2021 - [c13]Yuexiang Li, Yanping Wang, Guang Lin, Yi Lin, Dong Wei, Qirui Zhang, Kai Ma, Guangming Lu, Zhiqiang Zhang, Yefeng Zheng:
Triplet-Branch Network with Prior-Knowledge Embedding for Fatigue Fracture Grading. MICCAI (5) 2021: 449-458 - [c12]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 - [i35]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) - [i34]Aoxue Chen, Guang Lin:
Robust data-driven discovery of partial differential equations with time-dependent coefficients. CoRR abs/2102.01432 (2021) - [i33]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) - [i32]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) - [i31]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) - [i30]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) - [i29]Wing Tat Leung, Guang Lin, Zecheng Zhang:
NH-PINN: Neural homogenization based physics-informed neural network for multiscale problems. CoRR abs/2108.12942 (2021) - [i28]Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang:
HEI: hybrid explicit-implicit learning for multiscale problems. CoRR abs/2109.02147 (2021) - [i27]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) - [i26]Haoyang Zheng, Ziyang Huang, Guang Lin:
PCNN: A physics-constrained neural network for multiphase flows. CoRR abs/2109.08965 (2021) - [i25]