Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations, such as finite element analysis (FEA), is a credible constitutive or material model. Calibration of these complex models is an essential step; however, the selection, calibration and validation of material models is often a discrete, multi-stage process that is decoupled from material characterization activities, which means the data collected does not always align with the data that is needed. To address this issue, an integrated workflow for delivering an enhanced characterization and calibration procedure (Interlaced Characterization and Calibration (ICC)) is introduced. This framework leverages Bayesian optimal experimental design (BOED) to select the optimal load path for a cruciform specimen in order to collect the most informative data for model calibration. The critical first piece of algorithm development is to demonstrate the active experimental design for a fast model with simulated data. For this demonstration, a material point simulator that models a plane stress elastoplastic material subject to bi-axial loading was chosen. The ICC framework is demonstrated on two exemplar problems in which BOED is used to determine which load step to take, e.g., in which direction to increment the strain, at each iteration of the characterization and calibration cycle. Calibration results from data obtained by adaptively selecting the load path within the ICC algorithm are compared to results from data generated under two naive static load paths that were chosen a priori based on human intuition. In these exemplar problems, data generated in an adaptive setting resulted in calibrated model parameters with reduced measures of uncertainty compared to the static settings.

翻译：高后果工程决策日益依赖计算模拟，而固体力学模拟（如有限元分析）的基础要素之一是可靠的本构模型或材料模型。这些复杂模型的校正是关键步骤；然而，材料模型的选择、校正与验证通常是离散的多阶段过程，与材料表征活动脱钩，这意味着收集的数据并不总是与所需数据一致。为解决此问题，本文提出一种集成工作流程，用于实现增强型表征与校正程序（交错表征与校正，简称ICC）。该框架利用贝叶斯最优实验设计（BOED）为十字形试件选择最优加载路径，以收集对模型校正最具信息量的数据。算法开发的首要关键步骤是演示针对快速模型使用模拟数据的主动实验设计。为此，我们选择了模拟双轴加载下平面应力弹塑性材料的物质点模拟器。ICC框架通过两个示例问题加以演示，其中BOED用于确定每一步加载的选择方式（例如，在校正与表征循环的每次迭代中，决定应变增量的方向）。通过ICC算法内自适应选择加载路径获得的校正结果，与基于人类直觉先验选择的两种朴素静态加载路径生成的数据校正结果进行比较。在这些示例问题中，自适应设置生成的数据所校正的模型参数，其不确定性度量低于静态设置。