EF1 – Extracting dynamical Laws from Complex Data



Machine Learning Enhanced Filtering Methods for Inverse Problems

Project Heads

Claudia Schillings

Project Members

N.N., Philipp Wacker (until 09/22)

Project Duration

01.03.2022 − 29.02.2024

Located at

FU Berlin


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.

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