Open AccessModeling of dynamical systems through deep learning
Author(s) -
P. Rajendra,
V. Brahmajirao
Publication year - 2020
Publication title -
biophysical reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.766
H-Index - 39
eISSN - 1867-2469
pISSN - 1867-2450
DOI - 10.1007/s12551-020-00776-4
Subject(s) - dynamical systems theory , computer science , artificial intelligence , machine learning , context (archaeology) , deep learning , curse of dimensionality , convolutional neural network , dimensionality reduction , reinforcement learning , nonlinear system , physics , paleontology , quantum mechanics , biology
This review presents a modern perspective on dynamical systems in the context of current goals and open challenges. In particular, our review focuses on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. We explore various challenges in modern dynamical systems, along with emerging techniques in data science and machine learning to tackle them. The two chief challenges are (1) nonlinear dynamics and (2) unknown or partially known dynamics. Machine learning is providing new and powerful techniques for both challenges. Dimensionality reduction methods are used for projecting dynamical methods in reduced form, and these methods perform computational efficiency on real-world data. Data-driven models drive to discover the governing equations and give laws of physics. The identification of dynamical systems through deep learning techniques succeeds in inferring physical systems. Machine learning provides advanced new and powerful algorithms for nonlinear dynamics. Advanced deep learning methods like autoencoders, recurrent neural networks, convolutional neural networks, and reinforcement learning are used in modeling of dynamical systems.