1 citations · 1 across the 5 of their papers we have counts for
6 papers
CI-PINN: Causal Integral Physics-Informed Neural Network for Solving Evolution Equations
Xiaodong Feng, Ziyu Sun, Tao Tang +2
Physics-informed neural networks (PINNs) solve partial differential equations (PDEs) by incorporating governing physical laws into the training loss. For evolution equations, howev…
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
Yueyang Wang, Xili Wang, Kejun Tang +3
Solving high-dimensional PDE-governed inverse problems is often challenging due to complex non-Gaussian posterior distributions, expensive forward model evaluations, and misspecifi…
Amortized Filtering and Smoothing with Conditional Normalizing Flows
Tiangang Cui, Xiaodong Feng, Chenlong Pei +2
Bayesian filtering and smoothing are central to data assimilation in nonlinear dynamical systems. Recent advances in deep generative models provide flexible approximations of the a…
A hybrid FEM-PINN method for time-dependent partial differential equations
Xiaodong Feng, Haojiong Shangguan, Tao Tang +2
In this work, we present a hybrid numerical method for solving evolution partial differential equations (PDEs) by merging the time finite element method with deep neural networks.…
DeepSPoC: A Deep Learning Based Sequential Propagation of Chaos
Kai Du, Yongle Xie, Tao Zhou +1
Classical particle methods based on propagation of chaos (PoC) have been developed for solving mean-field stochastic differential equations and their associated nonlinear Fokker--P…
Adaptive importance sampling for Deep Ritz
Xiaoliang Wan, Tao Zhou, Yuancheng Zhou
We introduce an adaptive sampling method for the Deep Ritz method aimed at solving partial differential equations (PDEs). Two deep neural networks are used. One network is employed…