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### Optimization of probability flows with applications to sampling and solving PDEs

**Mathematics of Data & Decisions**

Speaker: | Lingxiao Li, MIT |

Related Webpage: | https://people.csail.mit.edu/lingxiao/ |

Location: | Zoom https://ucdavis.zoom.us/j/97789573467 |

Start time: | Tue, Nov 21 2023, 1:10PM |

In this talk, I will explore the theme of optimization of probability flows based on my two recent papers, for the application of sampling and solving a class of mass-conserving partial differential equations (PDEs). For the first half of the talk, I will show that by considering a carefully designed mollified interaction energy that converges to the chi-squared divergence as the mollifying effect vanishes, we can turn the problem of sampling into an optimization problem in the probability space. Proper discretization of this energy then yields a simple and efficient particle-based sampling algorithm suitable for both unconstrained and constrained domains. For the second half of the talk, I will introduce a discretization-free optimization framework for solving a wide class of mass-conserving PDEs, including the Fokker-Planck equations. The main observation is that the velocity field of the probability flow needs to be self-consistent, which gives rise to an efficient iterative optimization scheme that scales up to high dimensions and achieves strong performance.