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基于相似性的多学科拓优化学习超参数预测器.

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  • 1Honda Research Institute Europe GmbH, 63073, Offenbach, Germany.

Scientific reports
|September 8, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了机器学习方法对拓优化 (TO),使工程师能够通过引用现有结构来控制设计. 这种方法显著降低了在复杂的工程任务中实现所需材料分布的计算成本.

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相关实验视频

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科学领域:

  • 工程 工程师 工程师 工程师
  • 计算科学 计算科学
  • 材料科学 材料科学 材料科学

背景情况:

  • 拓优化 (TO) 对于工业设计至关重要,它根据设计空间和负载提供最佳的材料分布.
  • 当前的TO方法与用户对最终设计的控制作斗争,特别是在具有相互矛盾标准的多学科任务中.
  • 现有的基于相似性的TO方法需要广泛的超参数采样,增加计算费用.

研究的目的:

  • 为基于相似性的拓优化开发一种全新的端到端方法.
  • 整合一个机器学习模型来预测超参数,降低计算成本.
  • 为工程师提供对TO设计的更大控制权,确保与参考结构相似.

主要方法:

  • 提出了一种基于端到端相似性的拓优化 (TO) 方法.
  • 集成了一个机器学习模型来预测TO方法的超参数.
  • 从一个学术线性弹性问题生成的训练数据用于模型概括.

主要成果:

  • 机器学习模型成功地预测了超参数,以最小的计算成本实现基于相似性的TO.
  • 该方法在线性弹性,非线性动态崩以及工业规模的TO问题上展示了概括性.
  • 成功应用于现实世界的汽车引擎盖框架设计问题,验证了工业适用性.

结论:

  • 拟议的机器学习综合方法在拓优化中增强了用户控制.
  • 这种方法显著降低了与实现所需的设计相似性相关的计算负担.
  • 这种方法对于现实世界的工业多学科设计问题是有效的,例如汽车零部件.