Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{GARBRECHT:2023:jmps,
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author = "Karl Garbrecht and Donovan Birky and Brian Lester and
John Emery and Jacob Hochhalter",
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title = "Complementing a continuum thermodynamic approach to
constitutive modeling with symbolic regression",
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journal = "Journal of the Mechanics and Physics of Solids",
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volume = "181",
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pages = "105472",
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year = "2023",
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ISSN = "0022-5096",
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DOI = "doi:10.1016/j.jmps.2023.105472",
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URL = "https://www.sciencedirect.com/science/article/pii/S0022509623002764",
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keywords = "genetic algorithms, genetic programming,
Physics-informed machine learning, Constitutive
modeling, Symbolic regression, Thermodynamics, Porous
material, Multi-tree GPSR, Multi-objective
optimization",
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abstract = "An interpretable machine learning method,
physics-informed genetic programming-based symbolic
regression (P-GPSR), is integrated into a continuum
thermodynamic approach to developing constitutive
models. The proposed strategy for combining a
thermodynamic analysis with P-GPSR is demonstrated by
generating a yield function for an idealized material
with voids, i.e., the Gurson yield function. First, a
thermodynamic-based analysis is used to derive model
requirements that are exploited in a custom P-GPSR
implementation as fitness criteria or are strongly
enforced in the solution. The P-GPSR implementation
improved accuracy, generalizability, and training time
compared to the same GPSR code without physics-informed
fitness criteria. The yield function generated through
the P-GPSR framework is in the form of a composite
function that describes a class of materials and is
characteristically more interpretable than GPSR-derived
equations. The physical significance of the input
functions learned by P-GPSR within the composite
function is acquired from the thermodynamic analysis.
Fundamental explanations of why the implemented P-GPSR
capabilities improve results over a conventional GPSR
algorithm are provided",
- }
Genetic Programming entries for
Karl Michael Garbrecht
Donovan Birky
Brian Lester
John M Emery
Jacob Dean Hochhalter
Citations