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An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems 期刊论文
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2020, 卷号: 28, 期号: 5, 页码: 2180-2205
作者:  Yan, Liang;  Zhou, Tao
收藏  |  浏览/下载：131/0  |  提交时间：2021/01/14
Bayesian inverse problems  deep neural networks  multi-fidelity surrogate modeling  Markov chain Monte Carlo  
Efficient Stochastic Galerkin Methods for Maxwell's Equations with Random Inputs 期刊论文
JOURNAL OF SCIENTIFIC COMPUTING, 2019, 卷号: 80, 期号: 1, 页码: 248-267
作者:  Fang, Zhiwei;  Li, Jichun;  Tang, Tao;  Zhou, Tao
收藏  |  浏览/下载：159/0  |  提交时间：2020/01/10
Maxwell's equations  Finite element method  Random inputs  Polynomial chaos methods  Stochastic Galerkin  
Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 卷号: 381, 页码: 110-128
作者:  Yan, Liang;  Zhou, Tao
收藏  |  浏览/下载：166/0  |  提交时间：2019/03/11
Bayesian inverse problems  Multi-fidelity polynomial chaos  Surrogate modeling  Markov chain Monte Carlo  
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：161/0  |  提交时间：2019/03/11
Uncertainty quantification  Data-driven polynomial chaos expansions  Weighted least-squares  Equilibrium measure