Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering (ICME) toolboxes that relates microstructures to homogenized materials properties and establishes the structure-property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. In this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304L, Tantalum, and Cantor high-entropy alloy.

翻译：晶体塑性有限元模型（CPFEM）是集成计算材料工程（ICME）工具库中一种强大的数值模拟方法，它能够将微观结构与均匀化材料性能相关联，并在计算材料科学中建立结构-性能之间的关联。然而，要建立预测能力，需要标定底层本构模型、验证求解过程，并根据实验数据验证模型预测。贝叶斯优化（BO）作为一种无梯度的全局高效优化算法脱颖而出，能够用于标定CPFEM的本构模型。本文采用一种近期发展的异步并行约束贝叶斯优化算法，对不锈钢304L、钽以及Cantor高熵合金的唯象本构模型进行了标定。