Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications

arXiv:2310.20425v3 Announce Type: replace
Abstract: The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of physics-enhanced machine learning methods, expressed across the defining axes of physics and data, is discussed by engaging in a comprehensive exploration of its characteristics, usage, and motivations. In doing so, we present a survey of recent applications …

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