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.

翻译：本立场论文广泛探讨了物理增强机器学习（PEML）——亦称科学机器学习——特别关注为应对动力系统挑战而开发的PEML策略。超越传统机器学习策略的必要性源于：（i）有效数据量有限；（ii）避免产生精确但错误的预测；（iii）处理不确定性；（iv）提供可解释与可理解的推断。本文通过考虑四种物理及领域知识偏置给出了PEML的通用定义，并讨论了三大类PEML方法：物理引导型、物理编码型与物理信息型。最后阐述了在涉及复杂动力系统的工程应用中，发展PEML策略以指导高风险决策的优势与挑战。