Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
It proposes a framework for improving decision-making in engineering applications with complex dynamical systems, but it is incremental as it synthesizes existing ideas without presenting new results.
This position paper examines Physics-Enhanced Machine Learning (PEML) for dynamical systems, addressing challenges like limited data and inaccurate predictions by categorizing approaches into physics-guided, physics-encoded, and physics-informed methods.
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.