Digital twins require high-quality data to achieve predictive capability, but time and resource limitations make efficient experiment design essential. Model-based design of experiments can address this challenge, especially when coupled with equation-oriented optimization and first-principles models. Pyomo.DoE is a software package for optimal experimental design of high-fidelity, equation-oriented models; however, embedding linear algebra operations such as matrix inversion and eigenvalue computation within these optimization problems remains difficult. This work extends Pyomo.DoE with callback-based capabilities that enable rigorous computation of eigenvalue-based design metrics, including minimum eigenvalue optimality (E-optimality) and condition number optimality (ME-optimality), within equation-oriented optimization frameworks. These additions allow experimental design to focus directly on poorly informed or numerically problematic parameter directions. We also present a new experiment-creation modeling abstraction for intrusive uncertainty quantification in Pyomo that reduces user modeling effort by aligning model and software abstractions across the digital twin workflow. In addition, a brief tutorial on experimental design metrics is provided in the methodology and supplementary information. Overall, this work expands the range of practical optimal design criteria available in Pyomo.DoE and improves the workflow for building and refining high-value digital twins.

翻译：数字孪生需要高质量数据以实现预测能力，但时间和资源限制使得高效实验设计至关重要。基于模型的实验设计能够应对这一挑战，特别是当与面向方程的优化和第一性原理模型相结合时。Pyomo.DoE是一个用于高保真面向方程模型最优实验设计的软件包；然而，将矩阵求逆和特征值计算等线性代数运算嵌入这些优化问题中仍然困难。本研究扩展了Pyomo.DoE的回调能力，使其能够在面向方程优化框架中严格计算基于特征值的设计指标，包括最小特征值最优性（E-最优性）和条件数最优性（ME-最优性）。这些新增功能允许实验设计直接关注信息不足或数值病态的参数方向。我们还为Pyomo中的侵入式不确定性量化提出了一种新的实验创建建模抽象，通过对齐数字孪生工作流中的模型和软件抽象来减少用户建模工作量。此外，在方法论和补充信息中提供了实验设计指标的简要教程。总体而言，本研究扩展了Pyomo.DoE中可用的实际最优设计准则范围，并改进了构建和优化高价值数字孪生的工作流程。