high-dimensional nonlinear systems. The proposed approach leverages physics-informed
machine learning to solve high-dimensional Hamilton-Jacobi-Bellman equations arising in
optimal feedback control. Concretely, we augment linear quadratic regulators with neural
networks to handle nonlinearities. We train the augmented models on data generated
without discretizing the state space, enabling application to high-dimensional problems. We …