  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.
