Machine learning surrogate emulators are needed in engineering design and optimization tasks to rapidly emulate computationally expensive physics-based models. In micromechanics problems the local full-field response variables are desired at microstructural length scales. While there has been a great deal of work on establishing architectures for these tasks there has been relatively little work on establishing microstructural experimental design strategies. This work demonstrates that intelligent selection of microstructural volume elements for subsequent physics simulations enables the establishment of more accurate surrogate models. There exist two key challenges towards establishing a suitable framework: (1) microstructural feature quantification and (2) establishment of a criteria which encourages construction of a diverse training data set. Three feature extraction strategies are used as well as three design criteria. A novel contrastive feature extraction approach is established for automated self-supervised extraction of microstructural summary statistics. Results indicate that for the problem considered up to a 8\% improvement in surrogate performance may be achieved using the proposed design and training strategy. Trends indicate this approach may be even more beneficial when scaled towards larger problems. These results demonstrate that the selection of an efficient experimental design is an important consideration when establishing machine learning based surrogate models.

翻译：机器学习代理仿真器在工程设计与优化任务中不可或缺，用于快速模拟计算成本高昂的物理模型。在微观力学问题中，需要获取微结构尺度下的局部全场响应变量。尽管已有大量研究致力于建立此类任务的网络架构，但关于微结构实验设计策略的工作相对较少。本研究表明，通过智能选取用于后续物理仿真的微结构体积单元，能够构建更精确的代理模型。建立合适框架面临两个关键挑战：(1) 微观结构特征量化；(2) 建立鼓励构建多样化训练数据集的准则。本研究采用三种特征提取策略及三种设计准则，并提出一种新型对比特征提取方法，用于自动自监督提取微结构汇总统计量。结果表明，对于所考虑的问题，采用所提出的设计与训练策略可使代理模型性能提升高达8%。趋势表明，该方法在扩展到更大规模问题时可能更具优势。这些结果证明，在建立基于机器学习的代理模型时，选取高效的实验设计是需要考虑的重要因素。