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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：129/0  |  提交时间：2021/01/14
Bayesian inverse problems  deep neural networks  multi-fidelity surrogate modeling  Markov chain Monte Carlo  
A Numerical Study of the 3-Periodic Wave Solutions to Toda-Type Equations 期刊论文
COMMUNICATIONS IN COMPUTATIONAL PHYSICS, 2019, 卷号: 26, 期号: 2, 页码: 579-598
作者:  Thang, Yingnan;  Hu, Xingbiao;  He, Yi;  Sun, Jianqing
收藏  |  浏览/下载：150/0  |  提交时间：2020/01/10
Toda-type equation  N-periodicwave solution  Riemann's theta-function  Gauss-Newton method  
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：131/0  |  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：140/0  |  提交时间：2019/03/05
Gradient-enhanced l(1) minimization  compressed sensing  sparse Fourier expansions  restricted isometry property  mutual incoherence