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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
Authors:  Yan, Liang;  Zhou, Tao
Favorite  |  View/Download:64/0  |  Submit date:2021/01/14
Bayesian inverse problems  deep neural networks  multi-fidelity surrogate modeling  Markov chain Monte Carlo  
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
Authors:  Yan, Liang;  Zhou, Tao
Favorite  |  View/Download:97/0  |  Submit date:2019/03/11
Bayesian inverse problems  Multi-fidelity polynomial chaos  Surrogate modeling  Markov chain Monte Carlo  
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
Authors:  Yan, Liang;  Zhou, Tao
Favorite  |  View/Download:93/0  |  Submit date:2020/01/10
Bayesian inverse problems  ensemble Kalman inversion  multifidelity polynomial chaos  surrogate modeling