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