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What is Neural Networks for HJ PDE Solutions about?

This document discusses using neural network architectures to solve high-dimensional Hamilton-Jacobi partial differential equations (PDEs). It proposes classes of neural networks that exactly represent solutions to certain HJ PDEs where the Hamiltonian and initial data are obtained from the neural network parameters. This allows efficiently evaluating solutions to some HJ PDEs in high dimensions without numerical approximations. The paper also presents numerical results solving inverse problems using these

Author

bakidoki

Language

EN