
arXiv: 1706.04061
SummaryWe formulate extensions to data‐riven computing for both distance‐minimizing and entropy‐maximizing schemes to incorporate time integration. Previous works focused on formulating both types of solvers in the presence of static equilibrium constraints. Here, formulations assign data points to a variable relevance depending on distance to the solution and on maximum‐entropy weighting, with distance‐minimizing schemes discussed as a special case. The resulting schemes consist of the minimization of a suitably defined free energy over phase space subject to compatibility and a time‐discretized momentum conservation constraint. We present selected numerical tests that establish the convergence properties of both types of data‐driven solvers and solutions.
Classification and discrimination; cluster analysis (statistical aspects), Data Science, G.1.8, FOS: Physical sciences, dynamics, Computational Physics (physics.comp-ph), 510, noisy data, model-free computing, Applications of mathematical programming, Energy minimization in equilibrium problems in solid mechanics, data science, Physics - Computational Physics
Classification and discrimination; cluster analysis (statistical aspects), Data Science, G.1.8, FOS: Physical sciences, dynamics, Computational Physics (physics.comp-ph), 510, noisy data, model-free computing, Applications of mathematical programming, Energy minimization in equilibrium problems in solid mechanics, data science, Physics - Computational Physics
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