Preprint Open access
Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders
Koopman operator theory provides a linear framework for analyzing nonlinear dynamical systems and has become a major tool for data-driven modeling. A central challenge, however, is that finite-dimensional approximations computed by methods such as extended dynamic mode decomposition (EDMD) require the dictionary to be …