  The project of physics discovery is often equivalent to finding the most
concise description of a physical system. The description with optimum
predictive capability for a dataset generated by a physical system is one that
minimizes both predictive error on the dataset and the complexity of the
description. The discovery of the governing physics of a system can therefore
be viewed as a mathematical optimization problem. We outline here a method to
optimize the description of arbitrarily complex physical systems by minimizing
the entropy of the description of the system. The Recursive Domain Partitioning
(RDP) procedure finds the optimum partitioning of each physical domain into
subdomains, and the optimum predictive function within each subdomain. Penalty
functions are introduced to limit the complexity of the predictive function
within each domain. Examples are shown in 1D and 2D. In 1D, the technique
effectively discovers the elastic and plastic regions within a stress-strain
curve generated by simulations of amorphous carbon material, while in 2D the
technique discovers the free-flow region and the inertially-obstructed flow
region in the simulation of fluid flow across a plate.
