小数据范围内随机材料微结构的自监督优化
Self-supervised optimization of random material microstructures in the small-data regime
摘要：
虽然过程-结构-属性链的前向和后向建模受到了材料界的广泛关注，但考虑不确定性的努力较少。这些来自众多的来源和他们的量化和集成在反演过程中是必不可少的，在满足材料的设计目标。本文的第一个贡献是一个灵活的，完全概率制定这样的优化问题，占的不确定性的过程中的结构和结构性能的联系，并能够识别最佳的，高维的，过程参数。我们采用了一个概率，数据驱动的替代品的结构性能的链接，加快计算，使非差分目标的处理。我们将其与一种新的主动学习策略相结合，即自我监督的数据收集，它显著提高了准确性，同时需要少量的训练数据。我们证明了它在优化两相随机介质的机械和热性能方面的有效性，但设想其适用性包括各种各样的微结构敏感的设计问题。
英文摘要：
While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials community, fewer efforts have taken into consideration uncertainties. Those arise from a multitude of sources and their quantification and integration in the inversion process are essential in meeting the materials design objectives. The first contribution of this paper is a flexible, fully probabilistic formulation of such optimization problems that accounts for the uncertainty in the process-structure and structure-property linkages and enables the identification of optimal, high-dimensional, process parameters. We employ a probabilistic, data-driven surrogate for the structure-property link which expedites computations and enables handling of non-differential objectives. We couple this with a novel active learning strategy, i.e. a self-supervised collection of data, which significantly improves accuracy while requiring small amounts of training data. We demonstrate its efficacy in optimizing the mechanical and thermal properties of two-phase, random media but envision its applicability encompasses a wide variety of microstructure-sensitive design problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
