most citedSolving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach

85 citations · 147 across the 2 of their papers we have counts for

collaborators

2 papers

math.OC2021★ 62 cited

Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networks

Mo Zhou, Jiequn Han, Jianfeng Lu

We propose a novel numerical method for high dimensional Hamilton--Jacobi--Bellman (HJB) type elliptic partial differential equations (PDEs). The HJB PDEs, reformulated as optimal…

cs.LG2020★ 85 cited

Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach

Jiequn Han, Jianfeng Lu, Mo Zhou

We propose a new method to solve eigenvalue problems for linear and semilinear second order differential operators in high dimensions based on deep neural networks. The eigenvalue…