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Monte Carlo fPINNs: Deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations 期刊论文
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2022, 卷号: 400, 页码: 17
作者:  Guo, Ling;  Wu, Hao;  Yu, Xiaochen;  Zhou, Tao
收藏  |  浏览/下载：89/0  |  提交时间：2023/02/07
Physics -informed neural networks  Fractional Laplacian  Nonlocal operators  Uncertainty quantification  

Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models

 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 卷号: 461, 页码: 18
作者:  Guo, Ling;  Wu, Hao;  Zhou, Tao
收藏  |  浏览/下载：170/0  |  提交时间：2023/02/07
Data -driven modeling  Normalizing flows  Uncertainty quantification  Random fields  
Stein variational gradient descent with local approximations 期刊论文
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2021, 卷号: 386, 页码: 20
作者:  Yan, Liang;  Zhou, Tao
收藏  |  浏览/下载：124/0  |  提交时间：2022/04/02
Stein variational gradient decent  Bayesian inference  Deep learning  Local approximation  
Optimal design for kernel interpolation: Applications to uncertainty quantification 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2021, 卷号: 430, 页码: 19
作者:  Narayan, Akil;  Yan, Liang;  Zhou, Tao
收藏  |  浏览/下载：176/0  |  提交时间：2021/04/26
Kernel interpolation  Fekete points  Cholesky decomposition with pivoting  Hermite interpolation  Uncertainty quantification  
AN ACCELERATION STRATEGY FOR RANDOMIZE-THEN-OPTIMIZE SAMPLING VIA DEEP NEURAL NETWORKS* 期刊论文
JOURNAL OF COMPUTATIONAL MATHEMATICS, 2021, 卷号: 39, 期号: 6, 页码: 848-864
作者:  Yan, Liang;  Zhou, Tao
收藏  |  浏览/下载：161/0  |  提交时间：2022/04/02
Bayesian inverse problems  Deep neural network  Markov chain Monte Carlo