A supervised learning scheme for computing Hamilton-Jacobi equation via density coupling

Jianbo Cui; Shu Liu; Haomin Zhou

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arXiv · 2401.15954

A supervised learning scheme for computing Hamilton-Jacobi equation via density coupling

Jianbo Cui·Shu Liu·Haomin Zhou

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Abstract

We propose a supervised learning scheme for the first order Hamilton--Jacobi PDEs in high dimensions. The scheme is designed by using the geometric structure of Wasserstein Hamiltonian flows via a density coupling strategy. It is equivalently posed as a regression problem using the Bregman divergence, which provides the loss function in learning while the data is generated through the particle formulation of Wasserstein Hamiltonian flow. We prove a posterior estimate on $L^1$ residual of the proposed scheme based on the coupling density. Furthermore, the proposed scheme can be used to describe the behaviors of Hamilton--Jacobi PDEs beyond the singularity formations on the support of coupling density. Several numerical examples with different Hamiltonians are provided to support our findings.

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Jianbo Cui, Shu Liu, Haomin Zhou. 2024-01-29. A supervised learning scheme for computing Hamilton-Jacobi equation via density coupling. https://arxiv.org/abs/2401.15954

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