Hamzi, Boumediene and Owhadi, Houman (2020) Learning dynamical systems from data: a simple cross-validation perspective. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20201109-155527819
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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.
| Item Type: | Report or Paper (Discussion Paper) | ||||||
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| 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. | ||||||
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| Record Number: | CaltechAUTHORS:20201109-155527819 | ||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20201109-155527819 | ||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
| ID Code: | 106570 | ||||||
| Collection: | CaltechAUTHORS | ||||||
| Deposited By: | George Porter | ||||||
| Deposited On: | 10 Nov 2020 15:05 | ||||||
| Last Modified: | 11 Jan 2022 22:55 |
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