Hessian-augmented polynomial regression using PMP-derived data reduces sample complexity for approximating value functions and recovering feedback laws in deterministic optimal control problems.
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Convergence of symmetric kernel collocation for nonlinear PDEs is established in the RKHS norm for both fill-distance and residual-greedy point selection, without assuming solution uniqueness.
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Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEs
Hessian-augmented polynomial regression using PMP-derived data reduces sample complexity for approximating value functions and recovering feedback laws in deterministic optimal control problems.
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On Symmetric Kernel Collocation for Nonlinear PDEs
Convergence of symmetric kernel collocation for nonlinear PDEs is established in the RKHS norm for both fill-distance and residual-greedy point selection, without assuming solution uniqueness.