基于数据物理驱动模型和转移学习的板结构振动噪声的重建
Hualun Zhou1,2, Xiaodong Song1,2, Yue Huang3
1School of Transportation, Southeast University, Nanjing, 211189, China.
The Journal of the Acoustical Society of America
|January 29, 2025
概括
本研究介绍了一个数据物理驱动的模型,用于在板结构中准确的振动和声场重建的转移学习. 改进后的模型显示了更好的性能和更快的融合,即使数据有限.
科学领域:
- 振动声学是一种振动声学.
- 结构健康监测 结构健康监测
- 计算力学 计算力学 计算力学
背景情况:
- 了解结构的振动声学特征对于工程应用至关重要.
- 准确识别和重建振动和声音场是复杂的挑战.
研究的目的:
- 开发一个高精度的数据物理驱动模型,与转移学习 (DPDT) 集成,用于板结构的振动和声场重建.
- 通过使用转移学习,提高模型在不同结构的概括能力.
主要方法:
- 结合Kirchhoff-Helmholtz积分方程与卷积神经网络.
- 利用物理信息来最大限度地减少数据需求.
- 综合转移学习以提高跨结构适用性.
主要成果:
- 与DPD模型相比,DPDT模型显示出更高的预测准确性,稳定性和更快的趋同.
- 实现了高的R2和正常化交叉相关性,具有低的正常化平均平方误差.
- 用有限的数据点在声音场重建中展示了强度和有效性.
结论:
- DPDT模型为振动和声场重建提供了强大而高效的方法.
- 该方法对实际工程应用具有重大潜力,包括桥梁振动和噪声控制.
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