通过整合泰勒扩展和深度神经网络来加快对具有地面反射的二维同地几何声学模型的计算速度
Jinfeng Gao1,2, Hehong Ma3, Dongqing Miao4
1School of Computer and Artificial Intelligence, Huanghuai University, Zhumadian, China.
Science progress
|July 28, 2025
概括
这项研究引入了一种结合泰勒扩展和深度神经网络 (DNN) 的新方法,以加快声学建模. 该方法有效地解决了声学问题的赫尔姆霍尔茨方程,提高了声音场分析的计算效率.
科学领域:
- 计算物理学的计算物理.
- 声学建模的模型.
- 数字方法 数字方法.
背景情况:
- 使用赫尔姆霍尔茨方程解决声学问题是计算密集的.
- 传统的边界元素方法 (BEM) 在离散频率上面临着高计算成本的挑战.
- 同位几何分析在模拟复杂形状方面具有优势,但需要高效的解决方法.
研究的目的:
- 开发一种加速方法来解决与地面反射的异地几何声学模型.
- 为了降低与BEM中频率依赖评估相关的计算成本.
- 为了有效地预测声学结果,利用深层神经网络.
主要方法:
- 将汉克尔函数近似的泰勒扩展与同位几何分析和边界元素方法 (BEM) 结合起来.
- 使用泰勒数列扩展将BEM方程解为频率依赖和独立的术语.
- 在模拟结果上训练深度神经网络 (DNN),以预测声音场行为.
主要成果:
- 拟议的方法显著加快了异地质声学模型的解决方案.
- 泰勒扩展有效地近似汉克尔函数,减轻计算负担.
- DNN模型准确地预测了声学结果,证明了声音场分析的有效性.
结论:
- 泰勒膨胀,同位测分析和DNN的综合方法为声学问题提供了准确而高效的解决方案.
- 该方法通过一个二维数值示例进行验证,确认其可行性.
- 这项工作为加速复杂的声学模拟提供了一个有希望的方向.
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