Abstract
In this article, we discuss some of the recent developments in applying machine learning (ML) techniques to nonlinear dynamical systems. In particular, we demonstrate how to build a suitable ML framework for addressing two specific objectives of relevance: prediction of future evolution of a system and unveiling from given time-series data the analytical form of the underlying dynamics. This article is written in a pedagogical style appropriate for a course in nonlinear dynamics or machine learning.
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Sayan Roy is a fifth year BS-MS student from Department of Physics at IISER Bhopal. His research interests are in nonlinear dynamics, statistical physics and machine learning.
Debanjan Rana is a fifth year BS-MS student from Department of Chemistry at IISER Bhopal. His research interests are in photovoltaics, spectroscopy and machine learning.
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Roy, S., Rana, D. Machine Learning in Nonlinear Dynamical Systems. Reson 26, 953–970 (2021). https://doi.org/10.1007/s12045-021-1194-0
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DOI: https://doi.org/10.1007/s12045-021-1194-0