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物理受约束数据驱动的变化方法用于差异建模.

Arif Masud1, Sharbel Nashar1, Shoaib Goraya1

  • 1Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, IL 61801, USA.

Computer methods in applied mechanics and engineering
|March 11, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种数据驱动的差异建模方法,该方法将传感器数据集成到基于物理的模型中. 这种方法有效地恢复了系统能量和基本频率,即使数据有限和模型不准确.

关键词:
数据驱动的建模.不一致性建模不一致性建模动态系统是动态系统.弹性动力学是指弹性动力学.物理限制的建模模型.变化的多尺度不连续的加勒金 (VMDG) 方法.变化衍生的损失函数变化嵌入式测量数据.

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科学领域:

  • 计算力学 计算力学 计算力学
  • 数据驱动建模数据驱动建模
  • 科学计算科学计算

背景情况:

  • 基于物理学的模型往往难以有效地整合现实世界的传感器数据.
  • 理论模型和实验测量之间的差异在动态系统中很常见.
  • 整合测量数据可以提高模型准确性和预测能力.

研究的目的:

  • 提出一种新的数据驱动差异建模 (DDV) 方法.
  • 为了证明测量数据的变量嵌入到基于物理的框架中.
  • 调查数据同化对动态系统模拟准确性的影响.

主要方法:

  • 开发了一种数据驱动的差异建模方法,该方法可变化地嵌入测量数据.
  • 基于物理学的增强模型,其损失函数来自理论和测量之间的残余.
  • 将该方法应用于线性弹性动力学,并结合了来自该领域子集的高保真数据.

主要成果:

  • 该DDV方法成功地将高保真度数据纳入了前模拟.
  • 分析表明,该方法可以回收目标系统的能量和基本频段.
  • 应变和动能时间历史被准确地回收到一个悬臂梁,即使在一个无阻尼的模型.

结论:

  • 数据驱动的差异建模方法有效地整合了稀疏的测量数据.
  • 该方法通过考虑模型差异来提高基于物理的模型的准确性.
  • 这种方法提供了一个强大的框架,用于分析嵌入式传感器数据的动态系统.