A Deep Second-Order Stochastic Residual Method for Fully Nonlinear Parabolic PDEs

• URL: http://arxiv.org/abs/2607.16730v1
• Date: Sat, 18 Jul 2026 09:43:15 GMT
• Title: A Deep Second-Order Stochastic Residual Method for Fully Nonlinear Parabolic PDEs
• Abstract summary: We introduce the Deep Second-Order Residual Method (D2SRM) for high-dimensional, Hessian-dependent fully nonlinear parabolic PDEs.<n>A single space-time network generates derivative-consistent approximations of the solution, gradient, and Hessian.<n>Experiments on a 100-dimensional manufactured benchmark compare terminal treatments, probe Hessian couplings inside and outside the proved small-gain range, and show decreasing errors as the time step decreases.
• Score: 0.9023847175654603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the Deep Second-Order Stochastic Residual Method (D2SRM) for high-dimensional, Hessian-dependent fully nonlinear parabolic PDEs. A single scalar space--time network generates derivative-consistent approximations of the solution, gradient, and Hessian, which are trained jointly through second-order Brownian one-step residuals and terminal value and gradient penalties. For globally Lipschitz equations with identity diffusion and sufficiently weak Hessian coupling, we establish well-posedness in a Brownian occupation space and develop a population-level convergence theory. Under additional regularity, an a posteriori estimate bounds the squared full-jet occupation error of any admissible candidate by the time step and its population objective. For approximate population minimizers, the error bound separates time discretization, neural approximation, and population suboptimality; when the latter two terms are $O(h)$, the full-jet occupation norm is $O(h^{1/2})$. Experiments on a 100-dimensional manufactured benchmark compare terminal treatments, probe Hessian couplings inside and outside the proved small-gain range, and show decreasing errors as the time step decreases. The code is available at https://github.com/ZZHPKU/D2SRM.

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