深度学习驱动的多晶微结构优化框架：面向竞争性强度需求
Deep learning driven framework for optimization of polycrystalline microstructures under competing strength requirements
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中文总结 AI 辅助
提出深度学习驱动框架，用3D U-Net代理CPFE模拟，结合无导数优化和MMD可靠性区域，高效优化多晶铜微结构以平衡层裂与屈服强度，误差小于3%，耗时降低四个数量级。
中文摘要 AI 辅助
多晶微结构的计算设计以提升力学性能为目标，需要在随机形态上进行重复的高保真模拟，这使得传统的晶体塑性有限元（CPFE）方法在优化中代价过高。本文开发了一个深度学习驱动的框架，用于在准静态与动态性能需求相互竞争的条件下优化多晶微结构。一个三维U-Net代理模型将多晶铜微结构直接映射为平板撞击模拟的全场速度历史，保留了评估动态性能、探究底层波相互作用及失效机制所需的空间和时间分辨率。该代理模型与随机微结构生成及无导数优化相结合，以高效探索设计空间。为在优化过程中监测代理模型的可靠性，我们引入了一种基于最大均值差异（MMD）的分布一致可靠性区域（DCRR），用以量化与训练数据的分布偏移。该框架在多个设计问题中得到了验证，包括层裂强度与屈服强度的约束优化以及逆向设计。最优设计通过CPFE模拟验证，所有优化案例的预测误差均小于3%，而代理模型驱动的优化相比直接基于CPFE的优化，累计评估时间减少了约四个数量级。尽管本文以针对层裂和屈服强度的多晶微结构设计为例进行展示，该框架广泛适用于高保真全场模拟代价过高，但空间和时间分辨的响应信息对于评估、解释和优化候选设计至关重要的优化问题。
英文摘要
Computational design of polycrystalline microstructures for enhanced mechanical performance requires repeated high-fidelity simulations over stochastic morphologies, rendering conventional crystal plasticity finite element (CPFE) approaches prohibitively expensive for optimization. Here, we develop a deep learning-driven framework for optimizing polycrystalline microstructures under competing quasi-static and dynamic performance requirements. A 3D U-Net surrogate maps polycrystalline copper microstructures directly to full-field velocity histories from plate-impact simulations, preserving the spatial and temporal resolution needed to evaluate dynamic performance and interrogate underlying wave interactions and failure mechanisms. The surrogate is coupled with stochastic microstructure generation and derivative-free optimization to efficiently navigate the design space. To monitor surrogate reliability during optimization, we introduce a distribution-consistent reliability region (DCRR) based on maximum mean discrepancy (MMD) to quantify distributional shift from the training data. The framework is demonstrated for multiple design problems, including constrained optimization of spall and yield strengths and inverse design. The optimal designs are validated using CPFE simulations with less than 3% prediction error across all optimization cases, while the surrogate-driven optimization reduces cumulative evaluation time by approximately four orders of magnitude compared with direct CPFE-based optimization. Although demonstrated here for the design of polycrystalline microstructures targeting spall and yield strengths, the framework is broadly applicable to optimization problems where high-fidelity full-field simulations are prohibitively expensive but spatially and temporally resolved response information is essential for evaluating, interpreting, and optimizing candidate designs.
发表机构
- Johns Hopkins University（约翰斯·霍普金斯大学）
- Hopkins Extreme Materials Institute（霍普金斯极端材料研究所）
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