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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Detalles Bibliográficos

Principais autores:	Shuaiyi Yang, Xuefeng Tang, Lei Deng, Pan Gong, Mao Zhang, Junsong Jin, Xinyun Wang
Formato:	Artigo
Idioma:	Inglês
Publicado:	MDPI AG 2023-01-01
Series:	Metals
Assuntos:	interpretable calibration crystal plasticity Bayesian surrogate genetic algorithm Bayesian neural network
Acceso en liña:	https://www.mdpi.com/2075-4701/13/1/166
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