EF1 – Extracting Dynamical Laws from Complex Data

Project

EF1-4

Extracting Dynamical Laws by Deep Neural Networks:
A Theoretical Perspective

Project Heads

Gitta Kutyniok, Frank Noé, Barbara Zwicknagl

Project Members

Alex Goeßmann (TU) 

Project Duration

01.01.2019 – 31.12.2021

Located at

TU Berlin

Description

This project will develop a profound theoretical understanding of neural networks for extracting dynamical laws from complex data, focusing predominantly on expressivity aspects. We aim to derive various optimality results for the design of the architecture and to analyze the sample complexity of this problem.

Project Webpages

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