Published November 10, 2020
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Learning dynamical systems from data: a simple cross-validation perspective
- Creators
- Hamzi, Boumediene
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Owhadi, Houman
Abstract
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 Eroğlu, Yoshito Hirata, Jeroen Lamb, Edmilson Roque, Gabriele Santin and Yuzuru Sato for useful comments.Attached Files
Submitted - 2007.05074.pdf
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2007.05074.pdf
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Additional details
- Eprint ID
- 106570
- Resolver ID
- CaltechAUTHORS:20201109-155527819
- 792919
- Marie Curie Fellowship
- FA9550-18-1-0271
- Air Force Office of Scientific Research (AFOSR)
- Created
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2020-11-10Created from EPrint's datestamp field
- Updated
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2023-06-02Created from EPrint's last_modified field