科学机器学习用于弹性和声波传播:神经操作员和物理引导的神经网络
Nafisa Mehtaj1, Sourav Banerjee1
1Integrated Material Assessment and Predictive Simulation Laboratory (iMAPS), Department of Mechanical Engineering, Molinaroli College of Engineering and Computing, University of South Carolina, Columbia, SC 29201, USA.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
科学机器学习 (SciML) 推进了波传播建模. 神经运算符 (NO) 提供了一种通用的方法,在结构性健康监测的通用化,推断速度和可扩展性方面优于传统方法.
科学领域:
- 计算物理学的计算物理.
- 机器学习是机器学习.
- 波浪力学 波浪力学
背景情况:
- 传统的波浪传播模型是计算密集的.
- 科学机器学习 (SciML) 将物理与神经网络相结合.
- 现有的方法,如物理信息神经网络 (PINNs) 有局限性.
研究的目的:
- 引入数据驱动的框架:物理引导的神经网络 (PgNN) 和神经操作员 (NO).
- 突出显示NOs作为波传播的通用SciML方法.
- 提前基于波的结构健康监测 (SHM) 使用NOs.
主要方法:
- 开发了物理引导的神经网络 (PgNN) 和神经操作员 (NO).
- 专注于数据驱动的框架作为物理密集型方法的替代方案.
- 在各种工程领域分析了现有的NO算法.
主要成果:
- 与传统方法相比,NOs表现出优越的概括性,更快的推理和更强大的可扩展性.
- NOs可以解决一个带有不同边界条件的部分微分方程 (PDEs) 家族.
- NOs提供了一个比PINNs和PgNNs更普遍的SciML方法.
结论:
- NOs显示出波传播建模和SHM的显著潜力.
- 基于NO的方法为实际的波浪建模提供了更好的性能.
- 确定了NOs在计算波力学中的局限性和未来研究方向.
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