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.

本篇综述研究物理增强的机器学习 (PEML) - 也被称为科学机器学习 - 特别关注开发用于解决动力系统挑战的 PEML 策略，讨论了 PEML 方法的三个广泛分类 (物理指导、物理编码和物理信息) 以及在涉及复杂动力系统的工程应用中开发 PEML 策略用于指导高风险决策的优势和挑战。