Flow-based generative models for data assimilation

Abstract: Flow-based and diffusion generative models have emerged as powerful tools for sampling from complex, high-dimensional distributions, such as those found in image generation. In weather forecasting, they enable the generation of ensemble forecasts at a fraction of the computational cost of traditional numerical models. These models have also shown promise for solving inverse problems like data assimilation, offering advantages over classical methods in high-dimensional, nonlinear settings. In this talk, I will introduce the core ideas behind these approaches and present some of our recent results.

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

( paper )

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.