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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：134/0  |  提交时间：2021/01/14
Bayesian inverse problems  deep neural networks  multi-fidelity surrogate modeling  Markov chain Monte Carlo  
Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units 期刊论文
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2020, 卷号: 27, 期号: 2, 页码: 379-411
作者:  Li, Bo;  Tang, Shanshan;  Yu, Haijun
收藏  |  浏览/下载：127/0  |  提交时间：2020/05/24
Deep neural network  high dimensional approximation  sparse grids  rectified linear unit  rectified power unit  rectified quadratic unit  
Hermite Spectral Collocation Methods for Fractional PDEs in Unbounded Domains 期刊论文
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2018, 卷号: 24, 期号: 4, 页码: 1143-1168
作者:  Tang, Tao;  Yuan, Huifang;  Zhou, Tao
收藏  |  浏览/下载：136/0  |  提交时间：2019/03/05
Fractional PDEs  Hermite polynomials/functions  unbounded domain  spectral collocation methods  
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：155/0  |  提交时间：2019/03/05
Gradient-enhanced l(1) minimization  compressed sensing  sparse Fourier expansions  restricted isometry property  mutual incoherence