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校准对于基于计算的工程决策至关重要。当前的表征与校准方法存在以下问题：(1) 使用简化的试样几何形状和全局数据，(2) 无法保证为特定目标模型收集到足够的表征数据，(3) 采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) 流程呈顺序化、不灵活且耗时。本研究整合了多项最新进展，提出了一种名为"交织式表征与校准"的改进工作流程，推动了本构模型校准的技术前沿。ICC范式具有以下特点：(1) 高效利用全场数据校准高保真材料模型，(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) 将这些进展整合到准实时反馈循环中。本文通过模拟铝合金十字形试样双轴变形的全场数据校准材料模型，展示了ICC框架的应用。采用贝叶斯最优实验设计主动驱动十字形试样沿近视最优载荷路径变形，该设计选择能产生最大期望信息增益的载荷步。为增强数值稳定性并节约计算资源，通过主成分分析对全场数据进行降维处理，并采用可近似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校准，从而支持可信的决策制定，并有望提升固体力学建模的敏捷性。