Machine learning surrogate models have emerged as a promising approach for accelerating multiscale materials simulations while preserving predictive fidelity. Among them, the Orientation-aware Interaction-based Deep Material Network (ODMN) provides a hierarchical homogenization framework in which material nodes encode crystallographic texture and interaction nodes enforce stress equilibrium under the Hill--Mandel condition. Trained solely on linear-elastic stiffness data, ODMN captures intrinsic microstructure--mechanics relationships, enabling accurate prediction of nonlinear mechanical responses and texture evolution. However, its applicability remains fundamentally limited by the absence of a parametric mapping from arbitrary microstructures to the ODMN parameter space. This limitation necessitates retraining for each new microstructure. To address this challenge, we reformulate ODMN generalization as a microstructure-to-parameter inference problem and propose the TACS--GNN--ODMN framework. The proposed framework combines a Texture-Adaptive Clustering and Sampling (TACS) scheme for texture representation with a Graph Neural Network (GNN) for inferring micromechanical equilibrium parameters. This strategy enables the construction of fully parameterized ODMNs for previously unseen microstructures without retraining. Numerical results demonstrate that the proposed framework accurately predicts nonlinear mechanical responses and texture evolution across diverse texture distributions. The predicted responses show close agreement with direct numerical simulations (DNS), highlighting the framework as a generalizable and physically interpretable surrogate model for microstructure-informed multiscale materials simulations.

翻译：机器学习替代模型已成为在保持预测保真度的同时加速多尺度材料模拟的一种有前景的方法。其中，基于取向感知交互的深度材料网络（ODMN）提供了一种层级均匀化框架，其中材料节点编码晶体学织构，而交互节点在希尔-曼德尔条件下强制满足应力平衡。仅以线弹性刚度数据训练，ODMN能够捕捉内在的微观结构-力学关系，从而准确预测非线性力学响应和织构演化。然而，其适用性从根本上受限于缺乏从任意微观结构到ODMN参数空间的参数化映射。这一限制要求针对每种新的微观结构进行重新训练。为解决这一问题，我们将ODMN泛化重新定义为微观结构到参数的推理问题，并提出了TACS-GNN-ODMN框架。该框架结合了用于织构表示的纹理自适应聚类与采样（TACS）方案和用于推断微力学平衡参数的图神经网络（GNN）。这种策略使得能够为先前未见过的微观结构构建完全参数化的ODMN，而无需重新训练。数值结果表明，所提出的框架能够准确预测不同织构分布下的非线性力学响应和织构演化。预测响应与直接数值模拟（DNS）结果高度一致，凸显了该框架作为微观结构信息多尺度材料模拟中一种可泛化且物理可解释的替代模型的潜力。