  Physics-based simulations typically operate with a combination of complex
differentiable equations and many scientific and geometric inputs. Our work
involves gathering data from those simulations and seeing how well tree-based
machine learning methods can emulate desired outputs without "knowing" the
complex backing involved in the simulations. The selected physics-based
simulations included Navier-Stokes, stress analysis, and electromagnetic field
lines to benchmark performance as numerical and statistical algorithms. We
specifically focus on predicting specific spatial-temporal data between two
simulation outputs and increasing spatial resolution to generalize the physics
predictions to finer test grids without the computational costs of repeating
the numerical calculation.
