AI 中文总结
研究提出用拓扑优化识别对本构模型发现最具信息性的实验配置的框架，紧密结合建模与实验，利用全场测量数据及模型不确定性指导实验设计，通过贝叶斯最优实验设计与拓扑优化结合实现，量化预期信息增益驱动试样几何拓扑优化。
AI 中文摘要
本构关系完善了连续介质力学方程，在设计和工程过程中作为材料的替代物。它们常以参数化形式指定，参数通过实验确定。本文提出一个框架，用于识别对本构模型发现最具信息性的实验配置。该框架将建模与实验紧密结合：模型利用全场测量的高维数据，模型中的当前不确定性指导未来实验设计。通过将贝叶斯最优实验设计与拓扑优化相结合来实现这一目标，贝叶斯设计准则量化预期信息增益，驱动试样几何形状的拓扑优化。
英文摘要
Constitutive relations close the equations of continuum mechanics, and serve as a surrogate for a material in the design and engineering process. They are often specified in a parameterized form with parameters identified by experiment. In this paper, we propose a framework for identifying experimental configurations that are maximally informative for material parameter discovery. The framework strongly couples modeling and experimentation: the model leverages high-dimensional data from full-field measurements, while the current uncertainty in the model guides the design of future experiments. We formulate this goal by integrating Bayesian optimal experimental design with topology optimization. The Bayesian design criterion quantifies expected information gain, which drives the topology optimization of the specimen geometry.
