Learning dynamically inspired bases for Koopman and transfer operator approximation

ArXi:2505.05085v3 Announce Type: replace-cross Transfer and Koopman operator methods offer a framework for representing complex, nonlinear dynamical systems via linear transformations, enabling a deeper understanding of the underlying dynamics. The spectra of these operators provide important insights into system predictability and emergent behaviour, although efficiently estimating them from data can be challenging.