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
Selected Publications
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