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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：123/0  |  提交时间：2023/02/07
Data -driven modeling  Normalizing flows  Uncertainty quantification  Random fields  
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：139/0  |  提交时间：2021/04/26
Kernel interpolation  Fekete points  Cholesky decomposition with pivoting  Hermite interpolation  Uncertainty quantification  
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：163/0  |  提交时间：2019/03/11
Uncertainty quantification  Data-driven polynomial chaos expansions  Weighted least-squares  Equilibrium measure  
AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS 期刊论文
INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2019, 卷号: 9, 期号: 3, 页码: 205-220
作者:  Yan, Liang;  Zhou, Tao
收藏  |  浏览/下载：158/0  |  提交时间：2020/01/10
Bayesian inverse problems  ensemble Kalman inversion  multifidelity polynomial chaos  surrogate modeling  
A Gradient-Enhanced l(1) Approach for the Recovery of Sparse Trigonometric Polynomials 期刊论文
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2018, 卷号: 24, 期号: 1, 页码: 286-308
作者:  Xu, Zhiqiang;  Zhou, Tao
收藏  |  浏览/下载：151/0  |  提交时间：2019/03/05
Gradient-enhanced l(1) minimization  compressed sensing  sparse Fourier expansions  restricted isometry property  mutual incoherence  
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：151/0  |  提交时间：2018/07/30
uncertainty quantification  least-squares approximations  Fekete points  QR decomposition  
A GENERALIZED SAMPLING AND PRECONDITIONING SCHEME FOR SPARSE APPROXIMATION OF POLYNOMIAL CHAOS EXPANSIONS 期刊论文
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2017, 卷号: 39, 期号: 3, 页码: A1114-A1144
作者:  Jakeman, John D.;  Narayan, Akil;  Zhou, Tao
收藏  |  浏览/下载：108/0  |  提交时间：2018/07/30
uncertainty quantification  polynomial chaos  compressed sensing  
A multilevel finite element method for Fredholm integral eigenvalue problems 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2015, 卷号: 303, 页码: 173-184
作者:  Xie, Hehu;  Zhou, Tao
收藏  |  浏览/下载：106/0  |  提交时间：2018/07/30
Uncertainty quantification  Karhunen-Loeve expansion  Fredholm eigenvalue problem  Multigrid finite element  
Weighted discrete least-squares polynomial approximation using randomized quadratures 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2015, 卷号: 298, 页码: 787-800
作者:  Zhou, Tao;  Narayan, Akil;  Xiu, Dongbin
收藏  |  浏览/下载：108/0  |  提交时间：2018/07/30
Least squares method  Orthogonal polynomials  Generalized polynomial chaos  Uncertainty quantification