Surrogate Modeling of Dynamics From Sparse Data Using Maximum Entropy Basis Functions
Dynamical Systems
2019-11-11 v1 Optimization and Control
Abstract
In this paper we present a data driven approach for approximating dynamical systems. A dynamics is approximated using basis functions, which are derived from maximization of the information-theoretic entropy, and can be generated directly from the data provided. This approach has advantages over other methods, where a dictionary of basis functions have to be provided by the user, which is non trivial in some applications. We compare the accuracy of the proposed data-driven modeling approach to existing methods in the literature, and demonstrate that for some applications the maximum entropy basis functions provide significantly more accurate models.
Cite
@article{arxiv.1911.03016,
title = {Surrogate Modeling of Dynamics From Sparse Data Using Maximum Entropy Basis Functions},
author = {Vedang M. Deshpande and Raktim Bhattacharya},
journal= {arXiv preprint arXiv:1911.03016},
year = {2019}
}