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

• URL: http://arxiv.org/abs/2405.05987v2
• Date: Sat, 8 Jun 2024 18:49:34 GMT
• Title: Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
• Authors: Alice Cicirello,
• Abstract summary: Physics-Enhanced Machine Learning (PEML) is also known as Scientific Machine Learning. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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