MSDI3: Learning governing equations of dynamical systems from data
Chairs: Stefan Klus, Christof Schuette, Wei Zhang
Over the last years, various data-based methods have been developed for learning governing equations of dynamical systems in diverse areas of science and engineering. In particular, the sparse identification of nonlinear dynamical systems (SINDY) method uses sparsity regression to find the fewest terms in the governing equation that match the data, which has drawn considerable attentions and has been successfully applied to nonlinear dynamical systems in mechanics, fluid, biology, etc.
Besides, methods based on Koopman operator, tensor or machine learning techniques have also demonstrated their ability in learning or inferring nonlinear complex dynamics from data. This minisymposium aims to bring together experts from different fields in order to discuss recent advances in this direction, including both new computational algorithms and their successful applications in different areas.
Find abstracts for all talks here or linked individually below.
Alex Mauroy (University of Namur, Belgium):
Zoltan Tuza (Imperial College London, GB)
Jan-Hendrik Niemann (ZIB, Germany):
Patrick Gelß (FU Berlin, Germany)Tensor- and kernel-based recovery of dynamical systems
Alex Goeßmann (TU Berlin, Germany)Recovery of tensor-structured functions: Scalable approaches in high dimensions
Hayden Schaeffer (Carnegie Mellon University, USA)Function Approximation via Sparse Random Features