Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm

Interpretable Calibration of Crystal Plasticity Model Using a Bayesian Surrogate-Assisted Genetic Algorithm

The accurate calibration of material parameters in crystal plasticity models is essential for applying crystal plasticity (CP) simulations. Identifying these parameters usually requires unfeasible single-crystal experiments or expensive time costs due to the use of traditional genetic algorithm (GA)...

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Bibliographic Details
Main Authors: Shuaiyi Yang, Xuefeng Tang, Lei Deng, Pan Gong, Mao Zhang, Junsong Jin, Xinyun Wang
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Metals
Subjects:
interpretable calibration
crystal plasticity
Bayesian surrogate
genetic algorithm
Bayesian neural network
Online Access:https://www.mdpi.com/2075-4701/13/1/166

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