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.
A Survey on Machine Learning Applied to Dynamic Physical Systems
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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.
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cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Cost-effective Reduced-Order Modeling via Bayesian Active Learning
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.