【摘要】 多孔材料具有典型的非均质特征，宏观性能本质上受微观结构特征调控，因此，建立精确的微观结构表征体系并实现高保真数值建模是揭示材料构效关系的核心科学问题。本文提出了一种基于深度学习的多相各向异性多孔材料微观结构重构方法。该方法将深度卷积生成式对抗网络模型与切片采样策略相结合，并基于One-hot编码准则将复杂的多相重构问题分解为一系列两相重构任务，成功构建了多相各向异性多孔材料高效重构模型。同时，提出了将三点统计相关函数算法作为重构模型的高阶统计控制器，突破了传统二阶统计量的表征局限，实现了微观结构高阶特征的精准量化。本研究为材料性能预测与微观结构优化设计提供了重要的理论依据与工程应用指导。更多还原

【Abstract】 Porous materials exhibit typical heterogeneous characteristics, with their macroscopic properties fundamentally governed by microstructural features. Therefore, establishing an accurate microstructure characterization system and realizing high-fidelity numerical modeling were the core scientific issues to reveal the structure-activity relationship of materials. This study proposes a deep learning-based reconstruction method for multiphase anisotropic porous microstructure. By integrating a deep convolutional generative adversarial network model with a slice sampling strategy, and decomposing the complex multiphase reconstruction task into a series of two-phase reconstruction tasks based on the One-hot encoding principle, the proposed method efficiently reconstruct multiphase anisotropic porous materials. In addition, a three-point statistical correlation function algorithm is introduced as a high-order statistical controller for the reconstruction model, addressing the limitations of traditional second-order statistical descriptors and enabling precise quantification of high-order microstructural features. This work provides valuable theoretical insights and practical guidance for performance prediction and microstructure optimization of porous materials.更多还原