The design and development of materials with increased damage resilience is often impeded by the difficulty in establishing the precise linkages, with quantified uncertainty, between the complex details of the internal structure of materials and their potential for damage initiation. We present herein a novel machine-learning-based approach for establishing reduced-order models (ROMs) that relate the microstructure of a material to its susceptibility to damage initiation. This is accomplished by combining the recently established materials knowledge system framework with toolsets such as feedforward neural networks and variational Bayesian inference. The overall approach is found to be versatile for training scalable and accurate ROMs with quantified prediction uncertainty for the propensity to damage initiation for a variety of microstructures. The approach is applicable to a large class of challenges encountered in multiscale materials design efforts.

中文翻译：

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使用贝叶斯神经网络对双相复合材料中的损伤起始进行排序的降阶模型

具有更高损伤恢复能力的材料的设计和开发通常受到难以在材料内部结构的复杂细节与其损伤引发的潜力之间建立精确联系的困难和量化的不确定性。我们在此提出了一种新的基于机器学习的方法，用于建立降阶模型 (ROM)，该模型将材料的微观结构与其对损伤起始的敏感性相关联。这是通过将最近建立的材料知识系统框架与前馈神经网络和变分贝叶斯推理等工具集相结合来实现的。发现整体方法对于训练具有量化预测不确定性的可扩展且准确的 ROM 具有通用性，可用于各种微结构的损伤起始倾向。