  Machine learning in context of physical systems merits a re-examination of
the learning strategy. In addition to data, one can leverage a vast library of
physical prior models (e.g. kinematics, fluid flow, etc) to perform more robust
inference. The nascent sub-field of \emph{physics-based learning} (PBL) studies
the blending of neural networks with physical priors. While previous PBL
algorithms have been applied successfully to specific tasks, it is hard to
generalize existing PBL methods to a wide range of physics-based problems. Such
generalization would require an architecture that can adapt to variations in
the correctness of the physics, or in the quality of training data. No such
architecture exists. In this paper, we aim to generalize PBL, by making a first
attempt to bring neural architecture search (NAS) to the realm of PBL. We
introduce a new method known as physics-based neural architecture search
(PhysicsNAS) that is a top-performer across a diverse range of quality in the
physical model and the dataset.
