平均压力频率响应的前馈神经网络建模
A feedforward neural network for modelling of average pressure frequency response
摘要：
Helmholtz方程已被用于模拟谐波载荷下的声压场。如果要研究频率范围内的许多不同几何形状，那么通过求解亥姆霍兹方程来计算谐波声压场很快就变得不可行。我们提出了一种机器学习的方法，即一个前馈密集神经网络，计算平均声压的频率范围。通过数值计算平均声压的响应，通过压力的本征模分解，用有限元生成数据。我们分析近似的准确性，并确定需要多少训练数据，以达到一定的精度，在预测的平均压力响应。
英文摘要：
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.
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