带有人工神经网络的多孔材料的表征
Alessandro Casaburo1,2, Giuseppe Petrone1,2, Francesco Franco1,2
1Pasta-Lab, Industrial Engineering Department, University of Naples Federico II, Naples, 80125, Italy.
The Journal of the Acoustical Society of America
|February 10, 2025
概括
机器学习使用人工神经网络准确预测多孔材料的声学特性. 这种方法简化了复杂的建模,减少了约翰逊-香-阿拉德 (JCA) 参数估计的实验成本和时间.
科学领域:
- 声学材料科学 声学材料科学
- 计算物理学的计算物理.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确的材料声学建模对于预测性能和优化制造至关重要.
- 由于复杂的参数和苛刻的实验设置,对多孔介质的特征提出了挑战.
- 参数估计的传统反向方法是敏感的,昂贵的和耗时的.
研究的目的:
- 调查人工神经网络 (ANN) 在多孔材料中确定约翰逊-香-阿拉德 (JCA) 模型参数的有效性.
- 分析输入特征对ANN性能的影响,用于JCA参数预测.
- 开发和验证基于ANN的等级程序,以高效地估计JCA参数.
主要方法:
- 利用人工神经网络 (ANN) 来模拟多孔材料的声学特性.
- 分析输入特征之间的相互依存关系及其对网络准确性的影响.
- 开发一个分层的ANN架构,以改善参数预测.
- 使用实验数据验证拟议的方法.
主要成果:
- 机器学习,特别是ANN,在预测多孔材料的JCA参数方面表现出很高的有效性.
- 该研究确定了关键的输入特征及其对网络性能的相互影响.
- 层次ANN程序在JCA参数预测中实现了最小的错误.
- 拟议的方法为传统的反向方法提供了更有效的替代方案.
结论:
- 人工神经网络为孔隙材料的声学建模提供了一个强大的工具.
- 开发的分层ANN方法使准确和高效的JCA参数估计成为可能.
- 这种基于机器学习的策略可以显著减少与材料表征相关的实验负担.
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