Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7686

@Article{GARBRECHT:2023:jmps,

• author = "Karl Garbrecht and Donovan Birky and Brian Lester and John Emery and Jacob Hochhalter",
• title = "Complementing a continuum thermodynamic approach to constitutive modeling with symbolic regression",
• journal = "Journal of the Mechanics and Physics of Solids",
• volume = "181",
• pages = "105472",
• year = "2023",
• ISSN = "0022-5096",
• DOI = "doi:10.1016/j.jmps.2023.105472",
• URL = "https://www.sciencedirect.com/science/article/pii/S0022509623002764",
• keywords = "genetic algorithms, genetic programming, Physics-informed machine learning, Constitutive modeling, Symbolic regression, Thermodynamics, Porous material, Multi-tree GPSR, Multi-objective optimization",
• 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