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神经优化机器:一种神经网络优化方法及其在增材制造中的应用与物理引导学习
1Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.
概括
神经优化机器 (NOM) 优化神经网络 (NN) 模型的设计,整合物理和数据用于增材制造. 这种方法简化了训练和优化,在不确定性条件下增强了机械疲劳特性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
- 工程 工程师 工程师 工程师
背景情况:
- 神经网络 (NN) 在设计中越来越多地用于客观函数和约束,因此需要优化NN模型.
- 对有关设计变量的NN模型的优化对于高级工程应用至关重要.
研究的目的:
- 提出一个神经优化机器 (NOM),用于受约束的单/多目标优化.
- 将NN优化与增材制造 (AM) 过程属性模型和物理引导的机器学习相结合.
主要方法:
- 为优化设计NN架构,激活和丢失函数.
- 使用NN的反向传播算法,与AM模型无集成.
- 在不确定性下,在AM中应用物理引导的NN来预测疲劳性能和优化过程参数.
主要成果:
- NOM有效地处理受约束的优化问题,在更高维度的设计变量下显示最小的计算成本增加.
- 在优化AM处理参数以提高机械疲劳性能方面证明了成功的应用.
- 通过将物理/知识与数据驱动模型集成,实现了与物理兼容的工艺设计.
结论:
- 该NOM为NN培训和优化提供了一个统一的框架,消除了对单独优化流程的需求.
- 这种方法使得通过整合物理引导学习和数据驱动模型,可以设计具有优化机械疲劳特性的增强制造工艺.
- 该研究突出了AM中受约束过程优化的一种新方法,增强了结构完整性的应用.
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