相关实验视频
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Surrogate Model Development for Digital Experiments in Welding
Published on: March 28, 2025
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用神经代用模型和遗传算法对薄壁复合轴对称结构进行多目标优化
Bartosz Miller1, Leonard Ziemiański1
1Faculty of Civil and Environmental Engineering and Architecture, Rzeszow University of Technology, Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.
Materials (Basel, Switzerland)
|October 28, 2023
概括
本研究使用神经网络替代模型来有效优化复合外,整合模式形状识别和网络合集,以提高复杂工程设计的准确性和可靠性.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算力学 计算力学 计算力学
背景情况:
- 复合材料外提供优良的强度与重量比,但需要精确的参数优化.
- 像遗传算法这样的随机优化方法是计算密集的.
- 使用神经网络的替代模型可以有效地近似复杂的功能.
研究的目的:
- 调查神经网络代用模型的使用,用于复合外的多目标优化.
- 通过模式形状识别和网络合集来提高优化流程的准确性和可靠性.
- 评估拟议方法的计算效率和有效性.
主要方法:
- 深度神经网络被用作替代模型来近似输入参数-目标函数关系.
- 模式形状识别被纳入,以提高多标准优化的准确性.
- 网络组合被用来增强模型的稳定性和可靠性.
- 效率分析将计算成本与传统方法 (如蒙特卡洛模拟) 进行比较.
主要成果:
- 通过模式形状识别和网络合集增强的代用模型方法,证明了高准确性和可靠性.
- 该方法在处理复杂的输入参数和复杂的设计方面被证明是有效的.
- 在计算成本和准确性之间实现了有利的权衡.
结论:
- 网络集成作为替代模型和模式形状识别的集成显著提高了复合的多目标优化.
- 这种高效准确的方法对先进的工程设计和优化方法有广泛的影响.
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