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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：51/0  |  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：126/0  |  提交时间：2023/02/07
Data -driven modeling  Normalizing flows  Uncertainty quantification  Random fields  
Constructing Least-Squares Polynomial Approximations 期刊论文
SIAM REVIEW, 2020, 卷号: 62, 期号: 2, 页码: 483-508
作者:  Guo, Ling;  Narayan, Akil;  Zhou, Tao
收藏  |  浏览/下载：133/0  |  提交时间：2020/09/23
least-squares approximations  optimal sampling  polynomial approximations  
Data-driven polynomial chaos expansions: A weighted least-square approximation 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 卷号: 381, 页码: 129-145
作者:  Guo, Ling;  Liu, Yongle;  Zhou, Tao
收藏  |  浏览/下载：166/0  |  提交时间：2019/03/11
Uncertainty quantification  Data-driven polynomial chaos expansions  Weighted least-squares  Equilibrium measure  
WEIGHTED APPROXIMATE FEKETE POINTS: SAMPLING FOR LEAST-SQUARES POLYNOMIAL APPROXIMATION 期刊论文
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2018, 卷号: 40, 期号: 1, 页码: A366-A387
作者:  Guo, Ling;  Narayan, Akil;  Yan, Liang;  Zhou, Tao
收藏  |  浏览/下载：153/0  |  提交时间：2018/07/30
uncertainty quantification  least-squares approximations  Fekete points  QR decomposition