6 papers
FLUID: Flow-based Unified Inference for Dynamics
Tiangang Cui, Xiaodong Feng, Chenlong Pei +2
Bayesian filtering and smoothing for high-dimensional nonlinear dynamical systems are fundamental yet challenging problems in many areas of science and engineering. In this work, w…
Latent representation learning based model correction and uncertainty quantification for PDEs
Wenwen Zhou, Xiaodong Feng, Ling Guo +1
Model correction is essential for reliable PDE learning when the governing physics is misspecified due to simplified assumptions or limited observations. In the machine learning li…
Overcoming Spectral Bias via Cross-Attention
Xiaodong Feng, Tao Tang, Xiaoliang Wan +1
Spectral bias implies an imbalance in training dynamics, whereby high-frequency components may converge substantially more slowly than low-frequency ones. To alleviate this issue,…
LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process
Xiaodong Feng, Ling Guo, Xiaoliang Wan +3
We propose a novel probabilistic framework, termed LVM-GP, for uncertainty quantification in solving forward and inverse partial differential equations (PDEs) with noisy data. The…
Integral regularization PINNs for evolution equations
Xiaodong Feng, Haojiong Shangguan, Tao Tang +1
Evolution equations, including both ordinary differential equations (ODEs) and partial differential equations (PDEs), play a pivotal role in modeling dynamic systems. However, achi…
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.…