A Caltech Library Service

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 (Unpublished)

[img] PDF - Submitted Version
See Usage Policy.


Use this Persistent URL to link to this item:


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 Paper
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.
Funding AgencyGrant Number
Marie Curie Fellowship792919
Air Force Office of Scientific Research (AFOSR)FA9550-18-1-0271
Record Number:CaltechAUTHORS:20201109-155527819
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106570
Deposited By: George Porter
Deposited On:10 Nov 2020 15:05
Last Modified:11 Jan 2022 22:55

Repository Staff Only: item control page