How entropic regression beats the outliers problem in nonlinear system identification
Abstract
In this work, we developed a nonlinear System Identification (SID) method that we called Entropic Regression. Our method adopts an information-theoretic measure for the data-driven discovery of the underlying dynamics. Our method shows robustness toward noise and outliers, and it outperforms many of the current state-of-the-art methods. Moreover, the method of Entropic Regression overcomes many of the major limitations of the current methods such as sloppy parameters, diverse scale, and SID in high-dimensional systems such as complex networks. The use of information-theoretic measures in entropic regression has unique advantages, due to the Asymptotic Equipartition Property of probability distributions, that outliers and other low-occurrence events are conveniently and intrinsically de-emphasized as not-typical, by definition. We provide a numerical comparison with the current state-of-the-art methods in sparse regression, and we apply the methods to different chaotic systems such as the Lorenz System, the Kuramoto-Sivashinsky equations, and the Double-Well Potential.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2020
- Source ID
- 10.1063/1.5133386
Entities
People
- Abd AlRahman R AlMomani
- Erik Bollt
- Jie Sun
Organizations
- Army Research Office
- Clarkson University
- Defense Advanced Research Projects Agency
- Huawei
- Office of Naval Research
- Simons Foundation