most citedAdaptive neural network basis methods for partial differential equations with low-regular solutions

1 citations · 1 across the 2 of their papers we have counts for

collaborators

3 papers

math.NA2025

A derivative-free localized stochastic method for very high-dimensional semilinear parabolic PDEs

Shuixin Fang, Changtao Sheng, Bihao Su +1

We develop a mesh-free, derivative-free, matrix-free, and highly parallel localized stochastic method for high-dimensional semilinear parabolic PDEs. The efficiency of the proposed…

math.NA2025

Deep random difference method for high-dimensional quasilinear parabolic partial differential equations

Wei Cai, Shuixin Fang, Tao Zhou

Solving high-dimensional parabolic partial differential equations (PDEs) with deep learning methods is often computationally and memory intensive, primarily due to the need for aut…

math.NA2024★ 1 cited

Adaptive neural network basis methods for partial differential equations with low-regular solutions

Jianguo Huang, Haohao Wu, Tao Zhou

This paper aims to devise an adaptive neural network basis method for numerically solving a second-order semilinear partial differential equation (PDE) with low-regular solutions i…