Physical Systems Modeled Without Physical Laws

• URL: http://arxiv.org/abs/2207.13702v1
• Date: Tue, 26 Jul 2022 20:51:20 GMT
• Title: Physical Systems Modeled Without Physical Laws
• Authors: David Noever, Samuel Hyams
• Abstract summary: Tree-based machine learning methods can emulate desired outputs without "knowing" the complex backing involved in the simulations. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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