University of Birmingham > Talks@bham > Data Science and Computational Statistics Seminar > Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space

Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space

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If you have a question about this talk, please contact Hong Duong.

The first part of this presentation will review connections between problems in the optimal control of diffusion processes, Hamilton-Jacobi-Bellman equations and forward-backward SDEs, having in mind applications in rare event simulation and stochastic filtering. The second part will explain a recent approach based on divergences between probability measures on path space and variational inference that can be used to construct appropriate loss functions in a machine learning framework. This is joint work with Lorenz Richter.

This talk is part of the Data Science and Computational Statistics Seminar series.

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