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Learning dynamical systems from data: a simple cross-validation perspective

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 (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20201109-155527819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)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2007.05074arXivDiscussion Paper
ORCID:
AuthorORCID
Owhadi, Houman0000-0002-5677-1600
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.
Funders:
Funding AgencyGrant Number
Marie Curie Fellowship792919
Air Force Office of Scientific Research (AFOSR)FA9550-18-1-0271
Record Number:10.13140/RG.2.2.24823.44964
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:10 Nov 2020 15:05

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