EF1 – Extracting dynamical Laws from Complex Data

The project aims at combining novel techniques arising in machine learning with Kalman based filtering approaches for inverse problems. We will investigate subsampling strategies and surrogate enhanced variants to enhance performance in case of high dimensional data spaces and highly complex forward models. Strategies to incorporate constraints on the parameters will be developed by establishing the link to the Bayesian approach to inverse problems.