AI 中文总结
该研究构建力学驱动的对抗智能体评估拓扑优化中深度学习代理模型的脆弱性，发现初始噪声会引发机械失效，物理梯度调节未必提升鲁棒性，SIMP优化器可部分恢复性能，为鲁棒生成式设计智能体训练提供基础。
AI 中文摘要
拓扑优化（同时采用基于物理的方法与深度学习代理模型）是网络制造系统中生成式设计智能体的核心基础。尽管深度学习代理模型因在线设计生成速度快而被广泛采用，但本研究证明其在输入扰动下存在脆弱性。本研究提出一种基于力学的可靠性评估框架，构建针对生成式设计模型的对抗智能体。我们研究严格非侵入式威胁模型：仅在初始密度通道引入有界扰动，而物理边界条件、柔度梯度通道、网络架构及求解器程序保持不变。对U-Net、卷积及生成式架构的代理模型（采用不同物理梯度调节深度）的评估显示，有界初始噪声可引发灾难性机械失效，通过切断载荷路径与断开支撑使柔度提升数个数量级。此外，我们发现深度学习代理模型中融入更丰富的物理梯度调节，并不保证在所有代理模型族中呈现单调鲁棒性。最后，物理在环恢复实验表明，用受扰拓扑初始化经典SIMP优化器可减轻设计性能下降，在测试案例中大概率将柔度恢复至接近基线水平。这些发现表明，在弹性网络制造系统中，学习得到的代理模型应作为经物理验证的初始化器，而非完全替代基于物理的求解器。此外，所提出的对抗智能体为未来训练抗噪声与目标扰动的生成式设计智能体奠定基础。
英文摘要
Topology optimization, using both physic-based approaches and deep learning surrogates, serves as a cornerstone for generative design agents in cyber-manufacturing systems. While deep learning surrogates have gained widespread adoption due to their speed in online design generation, this work demonstrates their vulnerability under input perturbations. In this work, we present a mechanics-grounded reliability evaluation framework that formulates an adversarial agent targeting the generative design models. We investigate a strictly non-intrusive threat model where bounded perturbations are introduced exclusively to the initial-density channel, while physical boundary conditions, compliance-gradient channels, network architectures, and solver routines remain intact. Evaluating surrogate models across U-Net, convolutional, and generative architectures with varying physics-gradient conditioning depths demonstrates that bounded initialization noise can cause catastrophic mechanical failure, increasing compliance by multiple orders of magnitude through severed load paths and disconnected supports. Furthermore, we discover that incorporating richer physics-gradient conditioning in the deep learning surrogates does not guarantee monotonic robustness across surrogate families. Finally, physics-in-the-loop recovery demonstrates that initializing the classical SIMP optimizer with perturbed topologies mitigates design performance degradation, having a high probability of restoring compliance to near-baseline levels across tested instances. These findings demonstrate that learned surrogates should serve as physics-verified initializers instead of replacing physics-based solvers entirely in a resilient cyber-manufacturing system. Moreover, the proposed adversarial agent provides a foundation for future training generative design agents robust against noise and targeted perturbations.
