论文标题
用于建模平均压力频率响应的前馈神经网络
A feedforward neural network for modelling of average pressure frequency response
论文作者
论文摘要
Helmholtz方程已用于对谐波负载下的声压场进行建模。如果一个人想研究许多不同的几何形状，则通过求解Helmholtz方程来计算谐波声压场很快就会变得不可行。我们提出了一种机器学习方法，即馈电密度神经网络，用于计算频率范围内的平均声压。数据是通过有限元素通过数值来通过数值来计算平均声压的响应（通过压力的特征分解）来生成的。我们分析了近似值的准确性，并确定需要多少训练数据，以便在平均压力响应的预测中达到一定的准确性。
The Helmholtz equation has been used for modelling the sound pressure field under a harmonic load. Computing harmonic sound pressure fields by means of solving Helmholtz equation can quickly become unfeasible if one wants to study many different geometries for ranges of frequencies. We propose a machine learning approach, namely a feedforward dense neural network, for computing the average sound pressure over a frequency range. The data is generated with finite elements, by numerically computing the response of the average sound pressure, by an eigenmode decomposition of the pressure. We analyze the accuracy of the approximation and determine how much training data is needed in order to reach a certain accuracy in the predictions of the average pressure response.
