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A Survey on Machine Learning Applied to Dynamic Physical Systems

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arxiv 2009.09719 v2 pith:UIYJQC5V submitted 2020-09-21 cs.LG cs.NE

Sagar Verma This is my paper · ORCID
classification cs.LGcs.NE
keywords surveyelectriclearningmachinemodelingmotorsphysicalsystems
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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.

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  1. Cost-effective Reduced-Order Modeling via Bayesian Active Learning

    cs.LG 2025-06 conditional novelty 4.0 of 10

    An active learning framework for reduced-order models, BayPOD-AL, shows that an error-bounded acquisition function outperforms uncertainty sampling and random sampling on a 1D heat equation surrogate task.

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