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Deterministic particle flows for constraining SDEs - MaRDI portal

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Deterministic particle flows for constraining SDEs

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Publication:6381219

arXiv2110.13020MaRDI QIDQ6381219

Author name not available (Why is that?)

Publication date: 25 October 2021

Abstract: Devising optimal interventions for diffusive systems often requires the solution of the Hamilton-Jacobi-Bellman (HJB) equation, a nonlinear backward partial differential equation (PDE), that is, in general, nontrivial to solve. Existing control methods either tackle the HJB directly with grid-based PDE solvers, or resort to iterative stochastic path sampling to obtain the necessary controls. Here, we present a framework that interpolates between these two approaches. By reformulating the optimal interventions in terms of logarithmic gradients ( scores ) of two forward probability flows, and by employing deterministic particle methods for solving Fokker-Planck equations, we introduce a novel fully deterministic framework that computes the required optimal interventions in one shot.




Has companion code repository: https://github.com/dimitra-maoutsa/DeterministicParticleFlowControl

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