English(EN) Scalable Stress-Constrained Topology Optimization: A Super-resolution and Simultaneous Approach Based on Machine Learning

新的SNTO框架使用神经网络进行可扩展拓扑优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

开发了一个名为同步神经网络拓扑优化（SNTO）的新框架，以应对大规模拓扑优化（特别是应力约束问题）的计算需求。SNTO利用神经网络来处理状态、伴随和设计变量，并在一个包含连续伴随分析和自动微分的循环中联合优化它们。与传统的有限元方法相比，这种方法旨在生成具有更清晰边界和更少灰度区域的高分辨率设计。该框架设计用于可扩展性，采用硬件感知训练方案，将设计域划分为多个GPU，并切分子域以实现高效的内存管理。 AI

影响这个新框架可以通过利用机器学习进行拓扑优化，显著加快复杂的工程设计过程。

排序理由这是一篇详细介绍拓扑优化新计算框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Xiangyu Sun, Shirin Hosseinmardi, Ramin Bostanabad · 2026-10-22 04:00

Scalable Stress-Constrained Topology Optimization: A Super-resolution and Simultaneous Approach Based on Machine Learning

arXiv:2610.28703v1 Announce Type: new Abstract: Large-scale topology optimization (TO) remains computationally demanding, especially for stress-constrained problems. To address this challenge, we propose the simultaneous neural topology optimization (SNTO) framework where we repr…

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Scalable Stress-Constrained Topology Optimization: A Super-resolution and Simultaneous Approach Based on Machine Learning

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