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A second-order and nonuniform time-stepping maximum-principle preserving scheme for time-fractional Allen-Cahn equations 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2020, 卷号: 414, 页码: 16
Authors:  Liao, Hong-lin;  Tang, Tao;  Zhou, Tao
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Time-fractional Allen-Cahn equation  Alikhanov formula  Adaptive time-stepping strategy  Discrete maximum principle  Sharp error estimate  
Highly Accurate Numerical Schemes for Stochastic Optimal Control Via FBSDEs 期刊论文
NUMERICAL MATHEMATICS-THEORY METHODS AND APPLICATIONS, 2020, 卷号: 13, 期号: 2, 页码: 296-319
Authors:  Fu, Yu;  Zhao, Weidong;  Zhou, Tao
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Forward backward stochastic differential equations  stochastic optimal control  stochastic maximum principle  projected quasi-Newton methods  
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
Authors:  Li, Bo;  Tang, Shanshan;  Yu, Haijun
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Deep neural network  high dimensional approximation  sparse grids  rectified linear unit  rectified power unit  rectified quadratic unit  
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
Authors:  Fang, Zhiwei;  Li, Jichun;  Tang, Tao;  Zhou, Tao
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Maxwell's equations  Finite element method  Random inputs  Polynomial chaos methods  Stochastic Galerkin  
Quasi-Potential Calculation and Minimum Action Method for Limit Cycle 期刊论文
JOURNAL OF NONLINEAR SCIENCE, 2019, 卷号: 29, 期号: 3, 页码: 961-991
Authors:  Lin, Ling;  Yu, Haijun;  Zhou, Xiang
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Rare event  Non-gradient system  Quasi-potential  Limit cycle  Minimum action method  
Explicit Deferred Correction Methods for Second-Order Forward Backward Stochastic Differential Equations 期刊论文
JOURNAL OF SCIENTIFIC COMPUTING, 2019, 卷号: 79, 期号: 3, 页码: 1409-1432
Authors:  Yang, Jie;  Zhao, Weidong;  Zhou, Tao
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Deferred correction method  Second-order forward backward stochastic differential equations  Euler scheme  High-order rate of convergence  
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
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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
Authors:  Guo, Ling;  Liu, Yongle;  Zhou, Tao
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Uncertainty quantification  Data-driven polynomial chaos expansions  Weighted least-squares  Equilibrium measure  
NUMERICAL APPROXIMATION OF ELLIPTIC PROBLEMS WITH LOG-NORMAL RANDOM COEFFICIENTS 期刊论文
INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2019, 卷号: 9, 期号: 2, 页码: 161-186
Authors:  Wan, Xiaoliang;  Yu, Haijun
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Wiener chaos expansion  Wick product  stochastic elliptic PDE  uncertainty quantification  log-normal random coefficient  
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:6/0  |  Submit date:2020/01/10
Bayesian inverse problems  ensemble Kalman inversion  multifidelity polynomial chaos  surrogate modeling