The complex behavior of inelastic woven composites stems primarily from their inherent heterogeneity. Achieving accurate predictions of their linear and nonlinear responses, while considering their microstructures, appears feasible through the application of multi-scale modeling approaches. However, effectively incorporating these methodologies into real-scale applications, particularly within FE analyses, remains challenging due to the significant computational requirements they entail. To overcome this issue, while considering the scale effects, this study introduces an alternative approach based on Artificial Neural Networks (ANNs) to perform a macroscopic surrogate model of composites. This model, referred to as Multiscale Thermodynamics Informed Neural Networks (MuTINN), is founded on thermodynamic principles and introduces specific quantities of interest that serve as internal state variables at the macroscopic level. This captures efficiently the state and evolution laws governing the history-dependent behavior of these composites while retaining the thermodynamic admissibility and the physical interpretability of their overall responses. Moreover, to facilitate its numerical implementation within a FE code, a Meta-UMat has been developed, streamlining the application of multiscale FEMuTINN approach for composite structure computations. The prediction capabilities of the proposed approach is demonstrated across the material scales, exemplified through diverse instances of woven composite structures. These applications account for anisotropic yarn damage and an elastoplastic polymer matrix behavior. The numerical results and the related comparison with experimental findings and FE computations demonstrate remarkable consistency across a wide range of non-proportional loading paths. This promises a potential solution to alleviate the computational challenges associated with multiscale simulations of large-scale composite structures.

中文翻译：

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多尺度热力学信息神经网络 (MuTINN) 对编织复合结构进行快速、节俭的非弹性计算

非弹性编织复合材料的复杂行为主要源于其固有的异质性。通过应用多尺度建模方法，在考虑其微观结构的同时，实现对其线性和非线性响应的准确预测似乎是可行的。然而，将这些方法有效地融入到实际规模的应用中，特别是在有限元分析中，由于它们需要大量的计算要求，仍然具有挑战性。为了克服这个问题，在考虑尺度效应的同时，本研究引入了一种基于人工神经网络（ANN）的替代方法来执行复合材料的宏观替代模型。该模型被称为多尺度热力学信息神经网络（MuTINN），建立在热力学原理的基础上，并引入了在宏观层面作为内部状态变量的特定感兴趣量。这有效地捕获了控制这些复合材料的历史相关行为的状态和演化定律，同时保留了其整体响应的热力学可接受性和物理可解释性。此外，为了促进在 FE 代码中的数值实现，我们开发了 Meta-UMat，简化了多尺度 FEMuTINN 方法在复合结构计算中的应用。所提出方法的预测能力在材料尺度上得到了证明，并通过编织复合结构的不同实例进行了例证。这些应用解释了各向异性纱线损伤和弹塑性聚合物基体行为。数值结果以及与实验结果和有限元计算的相关比较表明，在各种非比例加载路径上都具有显着的一致性。这有望成为缓解与大型复合结构的多尺度模拟相关的计算挑战的潜在解决方案。