26 citations · 26 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Gradient-enhanced deep neural network approximations
Xiaodong Feng, Li Zeng
We propose in this work the gradient-enhanced deep neural networks (DNNs) approach for function approximations and uncertainty quantification. More precisely, the proposed approach…
cs.LG2022
Adaptive deep density approximation for fractional Fokker-Planck equations
Li Zeng, Xiaoliang Wan, Tao Zhou
In this work, we propose adaptive deep learning approaches based on normalizing flows for solving fractional Fokker-Planck equations (FPEs). The solution of a FPE is a probability…
cs.LG2021★ 26 cited
Solving time dependent Fokker-Planck equations via temporal normalizing flow
Xiaodong Feng, Li Zeng, Tao Zhou
In this work, we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck (TFP) equations. It is well known that solutions…