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Modeling and Similitude01:12

Modeling and Similitude

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Generalized Hooke's Law01:22

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对于异质数字材料的弱公式物理信息建模和优化.

Zhizhou Zhang1, Jeong-Ho Lee1, Lingfeng Sun1

  • 1Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720, USA.

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概括
此摘要是机器生成的。

这项研究引入了一种新的物理信息神经网络 (PINN) 方法,使用弱配方来建模不连续的数字材料. 这种方法可以加快材料设计的优化,而不需要预先训练的模型或灵敏度分析.

关键词:
数字化材料,数字化材料.神经运营者是神经运营者.基于物理学的神经网络.拓优化优化拓学的优化软配方的配方没有很强.

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科学领域:

  • 计算材料科学 计算材料科学
  • 机器学习应用 机器学习应用
  • 部分微分方程 部分微分方程

背景情况:

  • 材料结构设计的传统数值方法是计算密集的.
  • 基于梯度的优化需要特定的衍生和初始化.
  • 现有的基于物理学的神经网络 (PINNs) 与复合材料中常见的不连续性作斗争.

研究的目的:

  • 开发一种基于物理的机器学习方法,用于模拟不连续的数字材料.
  • 为了克服结构力学中标准PINNs的连续性限制.
  • 通过将物理知情损失与设计目标相结合,加速材料设计探索.

主要方法:

  • 取代了部分微分方程 (PDE) 余在物理知情培训过程中的弱公式.
  • 应用该方法来建模具有极端结构不连续性的数字材料.
  • 整合物理知情损失直接与设计目标进行交互式优化.

主要成果:

  • 提出的方法成功地模拟了具有极端结构不连续性的数字材料.
  • 在无数据材料替代模型中保持物理准确性.
  • 直接优化过程在不需要模型预训练的情况下加速.

结论:

  • 在PINNs中的弱公式有效地处理材料建模中的结构不连续性.
  • 这种无数据,加速优化方法增强了材料设计的探索.
  • 该方法为复杂的复合材料模拟提供了一个有希望的替代方案.