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Published November 10, 2020 | Submitted
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Learning dynamical systems from data: a simple cross-validation perspective


Regressing the vector field of a dynamical system from a finite number of observed states is a natural way to learn surrogate models for such systems. We present variants of cross-validation (Kernel Flows [31] and its variants based on Maximum Mean Discrepancy and Lyapunov exponents) as simple approaches for learning the kernel used in these emulators.

Additional Information

B. H. thanks the European Commission for funding through the Marie Curie fellowship STALDYS-792919 (Statistical Learning for Dynamical Systems). H. O. gratefully acknowledges support by the Air Force Office of Scientific Research under award number FA9550-18-1-0271 (Games for Computation and Learning). We thank Deniz Ero─člu, Yoshito Hirata, Jeroen Lamb, Edmilson Roque, Gabriele Santin and Yuzuru Sato for useful comments.

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