使用人工神经网络在1D声波晶体纳米光束中预测缓慢的声音模式和频段结构计算
Fu-Li Hsiao1, Yen-Tung Yang1, Wen-Kai Lin1
1Institute of Photonics, National Changhua University of Education, Changhua.
Science progress
|August 7, 2024
概括
神经网络通过预测带结构和共振模式来加快语音晶体分析,大大降低了与传统方法相关的计算成本.
科学领域:
- 声学 声学 声学 声学
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 音声晶体,是具有周期性材料排列的人工结构,表现出类似于半导体中的电子带隙的音声带隙.
- 这些带隙可以创建共振腔,在结构缺陷中限制特定的弹性共振模式.
- 分析音频带结构对于理解带隙变化和模式转移至关重要,但传统方法是计算密集的.
研究的目的:
- 提出和评估神经网络 (NN) 的使用,作为传统的,耗时的计算声波带结构方法的有效替代方案.
- 展示NN如何降低分析音声带间隙和弹性共振模式的计算成本.
主要方法:
- 使用有限元素方法生成语音带结构图的数据集.
- 在带结构图中提取的数据点上训练神经网络,将每个模式点视为独立的数据点.
- 开发二级神经网络架构,通过专注于特定的共振模式来提高预测准确性.
主要成果:
- 拟议的神经网络方法显著减少了分析音频带结构所需的时间和计算资源.
- 该方法有效地预测了声波带间隙和弹性共振模式中的频率变化.
- 一个专门的网络架构提高了模式预测的准确性.
结论:
- 神经网络提供了一种强大而高效的工具,可以加速对音声晶体的分析.
- 这种方法可以通过减少重复计算,显著降低声波晶体应用的研发障碍.
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