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Data Driven Computing with noisy material data sets
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DOI：10.1016/j.cma.2017.07.039.png)
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Abstract
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We formulate a Data Driven Computing paradigm, termed max-ent Data Driven Computing, that generalizes distance-minimizing Data Driven Computing and is robust with respect to outliers. Robustness is achieved by means of clustering analysis. Specifically, we assign data points a variable relevance depending on distance to the solution and on maximum-entropy estimation. The resulting scheme consists of the minimization of a suitably-defined free energy over phase space subject to compatibility and equilibrium constraints. Distance-minimizing Data Driven schemes are recovered in the limit of zero temperature. We present selected numerical tests that establish the convergence properties of the max-ent Data Driven solvers and solutions. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Data science
Big data
Approximation theory
Scientific computing
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