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To understand changes in physical systems and facilitate decisions,explaining how model predictions are made is crucial. We use model-basedinterpretability, where models of physical systems are constructed by composingbasic constructs that explain locally how energy is exchanged and transformed.We use the port Hamiltonian (p-H) formalism to describe the basic constructsthat contain physically interpretable processes commonly found in the behaviorof physical systems. We describe how we can build models out of the p-Hconstructs and how we can train them. In addition we show how we can imposephysical properties such as dissipativity that ensure numerical stability ofthe training process. We give examples on how to build and train models fordescribing the behavior of two physical systems: the inverted pendulum andswarm dynamics.

Interpretable machine learning models: a physics-based view