Development of interpretable, data-driven plasticity models with symbolic regression
Created by W.Langdon from
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- @Article{BOMARITO:2021:CS,
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author = "G. F. Bomarito and T. S. Townsend and
K. M. Stewart and K. V. Esham and J. M. Emery and J. D. Hochhalter",
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title = "Development of interpretable, data-driven plasticity
models with symbolic regression",
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journal = "Computer \& Structures",
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volume = "252",
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pages = "106557",
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year = "2021",
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ISSN = "0045-7949",
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DOI = "doi:10.1016/j.compstruc.2021.106557",
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URL = "https://www.sciencedirect.com/science/article/pii/S0045794921000791",
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keywords = "genetic algorithms, genetic programming, Plasticity,
Homogenization, Symbolic regression",
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abstract = "In many applications, such as those which drive new
material discovery, constitutive models are sought that
have three characteristics: (1) the ability to be
derived in automatic fashion with (2) high accuracy and
(3) an interpretable nature. Traditionally developed
models are usually interpretable but sacrifice
development time and accuracy. Purely data-driven
approaches are usually fast and accurate but lack
interpretability. In the current work, a framework for
the rapid development of interpretable, data-driven
constitutive models is pursued. The approach is
characterized by the use of symbolic regression on data
generated with micromechanical finite element models.
Symbolic regression is the search for equations of
arbitrary functional form which match a given dataset.
Specifically, an implicit symbolic regression technique
is developed to identify a plastic yield potential from
homogenized finite element response data. Through three
controlled test cases of varying complexity, the
approach is shown to successfully produce interpretable
plasticity models. The controlled test cases are used
to investigate the robustness and scalability of the
method and provide reasonable recommendations for more
complex applications. Finally, the recommendations are
used in the application of the method to produce a
porous plasticity model from data corresponding to a
representative volume element of voids within a metal
matrix",
- }
Genetic Programming entries for
Geoffrey F Bomarito
Tyler S Townsend
K M Stewart
K V Esham
John M Emery
Jacob Dean Hochhalter
Citations