A Survey on Machine Learning Applied to Dynamic Physical Systems

2020/09/21 by Sagar Verma, Verma, Sagar · 1 citation
Computer Science · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Power System Optimization and Stability

paper · pdf · doi:10.48550/arxiv.2009.09719

openalex publication_date 2020/09/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This survey is on recent advancements in the intersection of physical modeling and machine learning. We focus on the modeling of nonlinear systems which are closer to electric motors. Survey on motor control and fault detection in operation of electric motors has been done.

Citations

• Deep Hidden Physics Models: Deep Learning of Nonlinear Partial\n Differential Equations

Cited by

• Cost-effective Reduced-Order Modeling via Bayesian Active Learning

Related

• Machine Learning in Nonlinear Dynamical Systems
• Machine learning-based state maps for complex dynamical systems: applications to friction-excited brake system vibrations
• Machine learning-based state maps for complex dynamical systems: applications to friction-excited brake system vibrations
• Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part I)
• A Physics-informed Machine Learning-based Control Method for Nonlinear Dynamic Systems with Highly Noisy Measurements