Toward selecting optimal predictive multiscale models

Toward selecting optimal predictive multiscale models
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选择最佳预测多尺度模型

DOI:
10.1016/j.cma.2022.115517
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发表时间:
2022
影响因子:
7.2
通讯作者:
Faghihi, Danial
Faghihi, Danial
中科院分区:
工程技术1区
文献类型:
--
作者:
Tan, Jingye;Liang, Baoshan;Singh, Pratyush Kumar;Farrell-Maupin, Kathryn A.;Faghihi, Danial

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这项工作提出了一种系统策略,用于在物理系统的一组可能的机械模型中选择“最佳”预测计算模型。为此,通过引入一种用于设计特定于模型的验证实验的方法来扩展奥卡姆合理性算法(Farrell-Maupin 等人,2015),以提供描绘感兴趣的预测量特征的数据。利用贝叶斯推理和模型合理性,该框架自适应地平衡模型复杂性和有效性之间的权衡,同时考虑数据、模型参数和模型本身选择的不确定性,以做出可靠的计算预测。该框架的应用在多晶材料中尺寸相关塑性的离散连续多尺度建模中得到了证明,涉及位错动力学模拟和应变梯度塑性模型。这项研究表明,验证实验的有效性取决于模型的选择;因此,充分告知预测计算模型的验证数据集可能无法有效验证其他模型。此外,多尺度建模结果表明,在给定离散位错动力学数据的情况下,通过排除各向同性硬化机制,可以获得预测微机电系统变形的最佳应变梯度塑性模型。
This work presents a systematic strategy for selecting an “optimal” predictive computational model among a set of possible mechanistic models of a physical system. To this end, the Occam-Plausibility Algorithm (Farrell-Maupin et al., 2015) is extended by introducing a method for the design of model-specific validation experiments to provide data that portray the features of the prediction quantities of interest. Leveraging Bayesian inference and model plausibility, the framework adaptively balances the trade-off between complexity and validity of the models while taking into account the uncertainty in data, model parameters, and the choice of the model itself for making reliable computational predictions. An application of this framework is demonstrated in discrete-continuum multiscale modeling of size-dependent plasticity in polycrystalline materials, involving dislocation dynamics simulations and strain gradient plasticity models.This study suggests that the effectiveness of validation experiments relies upon the choice of the model; thus, a validation data set that adequately informs a predictive computational model may not be effective for validating other models. Additionally, the multiscale modeling results show that, given the discrete dislocation dynamics data, the optimal strain gradient plasticity model for predicting the deformation of a microelectromechanical system is obtained by excluding the isotropic hardening mechanism.
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影响因子: 2.6
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