基于物理信息的神经网络的稀疏波场重建
Bin Xu1, Yun Zou1, Gaofeng Sha2
1School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China.
Ultrasonics
|January 25, 2025
概括
这项研究引入了一种使用物理信息的神经网络的新方法,可以从激光超声波测试中的稀疏测量中重建完整的波场数据. 这大大减少了数据采集时间,同时保持了高精度.
科学领域:
- 材料科学 材料科学 材料科学
- 非破坏性测试是指非破坏性测试.
- 计算物理 计算物理
背景情况:
- 激光超声波 (LU) 设备对于内部材料的表征至关重要.
- 使用LU获取完整的波场数据是耗时的.
- 缩短获取时间对于实际的LU应用是必不可少的.
研究的目的:
- 开发一种方法来缩短LU测试中的数据采集时间.
- 从稀疏的测量中重建完整的波场数据.
- 提高内部物质信息检索的效率.
主要方法:
- 使用稀疏采样实验数据作为输入.
- 使用物理信息的神经网络 (PINNs).
- PINNs学习波传播特征,以重建整个波场.
主要成果:
- 实现了95%的重建准确度.
- 成功重建完整的波场数据,仅使用总测量的1/400.
- 证明了基于PINN的方法的有效性.
结论:
- 拟议的方法大大减少了对LU测试的数据采集时间.
- 这种技术适用于各种波场重建任务,包括在线监控.
- 基于物理学的神经网络为稀疏波场重建提供了有效的解决方案.
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