Accurate material characterization and model calibration are essential for computationally-supported engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called Interlaced Characterization and Calibration that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) efficiently uses full-field data to calibrate a high-fidelity material model, (2) aligns the data needed with the data collected with an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework is demonstrated on the calibration of a material model using simulated full-field data for an aluminum cruciform specimen being deformed bi-axially. The cruciform is actively driven through the myopically optimal load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain. To aid in numerical stability and preserve computational resources, the full-field data is dimensionally reduced via principal component analysis, and fast surrogate models which approximate the input-output relationships of the expensive finite element model are used. The tools demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modeling.

翻译：精确的材料表征与模型标定对于计算辅助的工程决策至关重要。当前的表征与标定方法存在以下局限：(1) 采用简化的试样几何构型与全局数据，(2) 无法保证针对特定目标模型收集到充分的表征数据，(3) 使用确定性方法仅提供最佳拟合参数值而缺乏不确定性量化，(4) 流程呈串行化、缺乏灵活性且耗时。本研究整合多项最新进展，提出一种名为"交织式表征与标定"的改进工作流程，推动了本构模型标定的前沿发展。ICC范式具有以下特征：(1) 高效利用全场数据标定高保真材料模型，(2) 通过最优实验设计协议使所需数据与采集数据相匹配，(3) 通过贝叶斯推断量化参数不确定性，(4) 将这些进展整合至准实时反馈回路中。本文通过铝合金十字形试样双轴变形的模拟全场数据标定材料模型，展示了ICC框架的有效性。该框架采用贝叶斯最优实验设计驱动十字形试样沿近视最优载荷路径加载，通过选择能产生最大期望信息增益的载荷步实现主动控制。为增强数值稳定性并节约计算资源，全场数据通过主成分分析进行降维处理，并采用可近似替代昂贵有限元模型输入输出关系的快速代理模型。所展示的工具表明，高保真本构模型能够以量化不确定性的方式实现高效可靠的标定，从而为可信决策提供支持，并有望提升固体力学建模的敏捷性。