EF1 – Extracting dynamical Laws from Complex Data



Wasserstein Gradient Flows for Generalised Transport in Bayesian Inversion

Project Heads

Martin Eigel, Claudia Schillings, Gabriele Steidl

Project Members

Robert Gruhlke

Project Duration

01.01.2023 − 31.12.2024

Located at

FU Berlin


Generalised gradient Wasserstein flows connect measure transport and interacting particle systems. The project combines the analysis of efficient numerical methods for gradient flows, associated SDEs and compressed functional approximations in the context of Bayesian inversion with parametric PDEs and image reconstruction tasks.

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