activity
20222025
most citedTackling the Curse of Dimensionality with Physics-Informed Neural Networks

166 citations · 397 across the 11 of their papers we have counts for

collaborators
Showing math.NAShow all

2 papers · 1 filter

math.NA2024★ 20 cited

Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks

Zheyuan Hu, Kenji Kawaguchi, Zhongqiang Zhang +1

Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional nu…

math.NA2024

Tensor neural networks for high-dimensional Fokker-Planck equations

Taorui Wang, Zheyuan Hu, Kenji Kawaguchi +2

We solve high-dimensional steady-state Fokker-Planck equations on the whole space by applying tensor neural networks. The tensor networks are a linear combination of tensor product…