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Machine Learning meets Numerical Analysis of PDEs

Invited Speakers:

  • Prof. Dr. Markus Bachmayr (Johannes Gutenberg-Universität Mainz): ‘Adaptive Sparse and Low-Rank Approximations for High-Dimensional Problems’
  • Prof. Dr. Arnulf Jentzen (University of Münster)
  • Prof. Dr. Gitta Kutyniok (Technische Universität Berlin): ‘Beating the Curse of Dimensionality: A Theoretical Analysis of Deep Neural Networks and Parametric PDEs’
  • Prof. Dr. Nathan Kutz (University of Washington): ‘Data-driven discovery of governing equations and laws in engineering, physics and biology’
  • Prof. Dr. Ivan Oseledets (Skoltech Faculty Moscow): ‘Constructive methods for multivariate function approximations using tensors and deep learning’
  • Prof. Dr. Philipp Petersen (University of Vienna): ‘A primer on approximation theory of neural networks’
  • Leon Sallandt (Technische Universität Berlin): ‘A simple implementation of the policy iteration for solving the HJB equation’
  • Prof. Dr. Tobias Schaeffter (Physikalisch-Technische Bundesanstalt Berlin): ‘Quantitative Magnetic Resonance Imaging’
  • Prof. Dr. Reinhold Schneider (Technische Universität Berlin): ‘Hamilton Jacobi Bellman Equation and Feedback Control of Partial Differential Equations’

Schedule Winter School PDF