通过SwinUnet进行两阶段的多目标拓优化方法,并增强了泛化
Cheng Xiang1,2, Airong Chen2, Hua Li3
1College of Civil Engineering, Fuzhou University, Fuzhou, 350116, China.
Scientific reports
|March 19, 2025
概括
本研究介绍了一种两阶段的多目标拓优化 (MOTO) 方法,集成数据驱动的学习和基于物理的精细化. 这种新的方法有效地产生了准确的结构设计,降低了计算成本和数据依赖.
科学领域:
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
- 设计中的人工智能
背景情况:
- 传统的拓优化方法通常专注于单一目标,并产生高计算成本,特别是在多目标问题上.
- 现有的数据驱动方法可能需要广泛的数据集,并与对各种设计条件的概括性斗争.
研究的目的:
- 开发一种新的两阶段多目标拓优化 (MOTO) 方法,将数据驱动的学习与基于物理的精细化相结合.
- 为了提高复杂,多目标场景的结构设计的计算效率和准确性.
- 在拓优化中减少数据依赖,同时提高概括能力.
主要方法:
- 使用约束编程制定了一个MOTO数学模型,考虑合规性,应力分布和材料使用.
- 一个带有转移窗口注意力机制和轻量级模块的神经网络被开发用于高效的特征提取.
- 采用了两阶段的训练过程:第一阶段使用自适应输入张量来实时预测接近最佳的几何形状,第二阶段应用了基于物理的精细化.
主要成果:
- 提出的方法在生成最佳结构设计方面实现了高精度和计算效率.
- 该模型在可变设计领域,边界条件和非形几何学方面展示了强大的概括能力.
- 观察到数据依赖性显著减少,需要最小的样本进行培训.
结论:
- 新的两阶段MOTO方法为复杂的多目标结构设计问题提供了有效的解决方案.
- 数据驱动式学习和基于物理的精细化整合推进了拓优化领域.
- 这种方法提供了新的见解,并促进了结构设计实践的实际进展.
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