EF1 – Extracting dynamical Laws from Complex Data

Project

EF1-19

Machine Learning Enhanced Filtering Methods for Inverse
Problems

Project Heads

Claudia Schillings

Project Members

Philipp Wacker

Project Duration

01.03.2022 − 29.02.2024

Located at

FU Berlin

Description

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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