More and more work and codes are developed to solve high-dimensional PDEs, especially with the tools from deep learning. Here we collect a few code repositories to present the advancement of the field and promote the exploration of new problems. Suggestions and contributions are welcome to this page, including the codes you would like to share relevant to high-dimensional PDEs.

Solving high-dimensional partial differential equations using deep learning (Deep BSDE solver)

Adaptive deep learning for high-dimensional Hamilton-Jacobi-Bellman equations

Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space

Numerically solving parametric families of high-dimensional Kolmogorov partial differential equations via deep learning

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

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