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arXiv · 2405.05987

Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations

Alice Cicirello
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Abstract

This position paper takes a broad look at Physics-Enhanced Machine Learning (PEML) -- also known as Scientific Machine Learning -- with particular focus to those PEML strategies developed to tackle dynamical systems' challenges. The need to go beyond Machine Learning (ML) strategies is driven by: (i) limited volume of informative data, (ii) avoiding accurate-but-wrong predictions; (iii) dealing with uncertainties; (iv) providing Explainable and Interpretable inferences. A general definition of PEML is provided by considering four physics and domain knowledge biases, and three broad groups of PEML approaches are discussed: physics-guided, physics-encoded and physics-informed. The advantages and challenges in developing PEML strategies for guiding high-consequence decision making in engineering applications involving complex dynamical systems, are presented.

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Alice Cicirello. 2024-05-08. Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations. https://doi.org/10.1088/1742-6596%2F2909%2F1%2F012034

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