Machine learning-based multi-objective optimization for efficient identification of crystal plasticity model parameters

DOI10.1016/j.cma.2022.115740OpenAlexW4309576654MaRDI QIDQ2679509

Yanyan Li

Publication date: 20 January 2023

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2022.115740

zbMATH Keywords

crystal plasticityparameter identificationmachine learningnumerical algorithms

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Statistical mechanics, structure of matter (82-XX)

Related Items (1)

An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models

Uses Software

• EGO
• DiceDesign
• DiceEval

Cites Work

• Computer implementations of iterative and non-iterative crystal plasticity solvers on high performance graphics hardware
• Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals
• Efficient global optimization of expensive black-box functions
• Rate-independent crystalline and polycrystalline plasticity, application to FCC materials
• Design and analysis of computer experiments. With comments and a rejoinder by the authors
• Deep drawing simulations using the finite element method embedding a multi-level crystal plasticity constitutive law: experimental verification and sensitivity analysis
• Identification of crystal plasticity model parameters by multi-objective optimization integrating microstructural evolution and mechanical data
• A numerical study into element type and mesh resolution for crystal plasticity finite element modeling of explicit grain structures
• Kriging metamodels-based multi-objective shape optimization applied to a multi-scale heat exchanger
• A survey on kriging-based infill algorithms for multiobjective simulation optimization
• A dislocation-based constitutive law for pure Zr including temperature effects
• Explicit incorporation of deformation twins into crystal plasticity finite element models
• Kernels for Vector-Valued Functions: A Review
• The determination of the elastic field of an ellipsoidal inclusion, and related problems
• A multi-GPU implementation of a full-field crystal plasticity solver for efficient modeling of high-resolution microstructures

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