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Learning and reasoning about physical phenomena is still a challenge inrobotics development, and computational sciences play a capital role in thesearch for accurate methods able to provide explanations for past events andrigorous forecasts of future situations. We propose a thermodynamics-informedactive learning strategy for fluid perception and reasoning from observations.As a model problem, we take the sloshing phenomena of different fluidscontained in a glass. Starting from full-field and high-resolution syntheticdata for a particular fluid, we develop a method for the tracking (perception)and analysis (reasoning) of any previously unseen liquid whose free surface isobserved with a commodity camera. This approach demonstrates the importance ofphysics and knowledge not only in data-driven (grey box) modeling but also inthe correction for real physics adaptation in low data regimes and partialobservations of the dynamics. The method presented is extensible to otherdomains such as the development of cognitive digital twins, able to learn fromobservation of phenomena for which they have not been trained explicitly.

A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids