Learning dynamical systems from data: a simple cross-validation perspective
Machine Learning
2021-04-07 v1 Dynamical Systems
Chaotic Dynamics
Computation
Machine Learning
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 \cite{Owhadi19} and its variants based on Maximum Mean Discrepancy and Lyapunov exponents) as simple approaches for learning the kernel used in these emulators.
Cite
@article{arxiv.2007.05074,
title = {Learning dynamical systems from data: a simple cross-validation perspective},
author = {Boumediene Hamzi and Houman Owhadi},
journal= {arXiv preprint arXiv:2007.05074},
year = {2021}
}
Comments
File uploaded on arxiv on Sunday, July 5th, 2020. Got delayed due to tex problems on ArXiv. Original version at https://www.researchgate.net/publication/342693818_Learning_dynamical_systems_from_data_a_simple_cross-validation_perspective