Woven composites are produced by interlacing warp and weft fibers in a pattern or weave style. By changing the pattern or material, the mechanical properties of woven composites can be significantly changed; however, the role of woven composite architecture (pattern, material) on the mechanical properties is not well understood. In this paper, we explore the relationship between woven composite architectures (weave pattern, weave material sequence) and the corresponding modulus through our proposed Physics-Constrained Neural Network (PCNN). Furthermore, we apply statistical learning methods to optimize the woven composite architecture to improve mechanical responses. Our results show that PCNN can effectively predict woven architecture for the desired modulus with much higher accuracy than several baseline models. PCNN can be further combined with feature-based optimization to determine the optimal woven composite architecture at the initial design stage. In addition to relating woven composite architecture to its mechanical responses, our research also provides an in-depth understanding of how architectural features govern mechanical responses. We anticipate our proposed frameworks will primarily facilitate the woven composite analysis and optimization process and be a starting point to introduce Physics knowledge-guided Neural Networks into the complex structural analysis.

翻译：在本文中,我们探索了交织复合结构(编织模式、编织材料序列)和通过拟议中的物理-心律训练神经网络(PCNN)建立相应的模模结构之间的关系。此外,我们运用统计学习方法优化编织的复合结构,以改进机械反应。我们的成果显示,PCNNN能够有效地预测预设的模型结构结构,其精度远高于若干基线模型。PCNNN可以进一步与基于地貌的优化相结合,以确定最初设计阶段的最佳交织综合结构。除了将交织的复合结构与其机械反应相联系外,我们的研究还深入了解了建筑特征如何指导机械反应。我们所拟议的框架将主要促进交织的复合结构分析和优化,并开始一个基于地貌的优化,以便确定最初设计阶段的最佳交织的复合结构结构结构结构结构。我们的研究还将深入了解建筑特征如何指导机械反应。我们所拟议的框架将主要促进结构化综合分析,并开始一个点,以便开始一个基础化分析。