#7857. Interpretable Machine Learning for Texture-Dependent Constitutive Models with Automatic Code Generation for Topological Optimization

October 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Industrial and Manufacturing Engineering;
Materials Science (all);
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Abstract:
Genetic programming-based symbolic regression (GPSR) is a machine learning method which produces symbolic models that can be readily interpreted. This study utilized GPSR to derive uniaxial texture-based constitutive models for an additively manufactured alloy which were evaluated in post hoc analyses.
Keywords:
Constitutive model; Parameter homogenization; Symbolic regression; Texture; Topology optimization; Viscoplastic

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