Web: http://arxiv.org/abs/2209.09977

Sept. 22, 2022, 1:11 a.m. | Samuel E. Otto, Sebastian Peitz, Clarence W. Rowley

cs.LG updates on arXiv.org arxiv.org

Data-driven models for nonlinear dynamical systems based on approximating the
underlying Koopman operator or generator have proven to be successful tools for
forecasting, feature learning, state estimation, and control. It has become
well known that the Koopman generators for control-affine systems also have
affine dependence on the input, leading to convenient finite-dimensional
bilinear approximations of the dynamics. Yet there are still two main obstacles
that limit the scope of current approaches for approximating the Koopman
generators of systems with actuation. …

arxiv math

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