INEUS solves high-dimensional PIDEs via iterative neural regression with single-jump sampling instead of full integral evaluation.
Deep learning for continuous-time stochas- tic control with jumps
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Neural actor-critic method for high-dimensional HJB PDEs converges in Sobolev space to an infinite-dimensional ODE whose fixed points solve the stochastic control problem under a convexity-like Hamiltonian assumption, with numerical success up to 200 dimensions.
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INEUS: Iterative Neural Solver for High-Dimensional PIDEs
INEUS solves high-dimensional PIDEs via iterative neural regression with single-jump sampling instead of full integral evaluation.
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Neural Actor-Critic Methods for Hamilton-Jacobi-Bellman PDEs: Asymptotic Analysis and Numerical Studies
Neural actor-critic method for high-dimensional HJB PDEs converges in Sobolev space to an infinite-dimensional ODE whose fixed points solve the stochastic control problem under a convexity-like Hamiltonian assumption, with numerical success up to 200 dimensions.