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科学机器学习用于引导波和表面声波 (SAW) 传播:PgNN,PeNN,PINN和神经操作员
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)
|March 17, 2025
概括
科学机器学习 (SciML),特别是物理信息神经网络 (PINNs),为波传播建模提供了强大的解决方案. 在模拟声波,弹性和引导波方面,PINNs有效地克服了传统的计算挑战.
科学领域:
- 计算物理学的计算物理.
- 科学机器学习 (SciML) 是指科学机器学习.
- 波浪传播建模的模拟.
背景情况:
- 由部分微分方程 (PDEs) 控制的波传播建模提出了多个规模和多个维度的挑战.
- 传统的计算方法面临高成本和严格的假设,限制了它们的效率和准确性.
- 科学机器学习 (SciML) 作为一种新型范式,将物理定律嵌入到神经网络中.
研究的目的:
- 探索SciML波传播方法的演变.
- 评估物理信息神经网络 (PINNs) 在模拟声学,弹性和引导波传播中的应用.
- 突出PINNs在克服传统波浪建模技术局限性的潜力.
主要方法:
- 使用物理信息神经网络 (PINNs) 作为一个灰色盒子预测模型.
- 将物理定律直接嵌入到神经网络架构中.
- 理论分析和基于问题的例子来展示PINN的能力.
主要成果:
- PINNs有效地解决了传统方法固有的离散错误和计算低效率.
- 在声学,弹性和引导波传播方面表现出强大的预测能力.
- PINNs提供了数据驱动的预测能力和遵守物理定律之间的平衡.
结论:
- PINNs代表了波传播建模的转型方法,克服了传统技术的关键局限性.
- 尽管面临优化和可扩展性等挑战,PINNs仍具有推动该领域发展的巨大潜力.
- 这项研究为未来的研究提供了基础,将PINNs应用于结构健康监测 (SHM) 和非破坏性评估 (NDE).
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