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.