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

Blending Diverse Physical Priors with Neural Networks