Deep learning for frequency response prediction of a multimass oscillator.

Noise prevention in product development is becoming more and more important due to health effects and comfort restrictions. In the product development process, costly and time‐consuming simulations are carried out over parameter spaces, for example, via the finite element method, in order to find quiet product designs. The solution of dynamic systems is limited in their maximum frequency and the size of the parameter space. Therefore, the substitution of high‐fidelity models by machine learning approaches is desirable. In this contribution, we consider an academic benchmark: Training a neural network to predict the frequency response of a multimass oscillator. Neural network architectures based on multilayer perceptrons and transformers are investigated and compared with respect to their accuracy in frequency response prediction. Our investigations suggest that the transformer architecture is better suited, in terms of accuracy and in terms of capability to handle multiple system configurations in a single model. The code of this work is available at https://eckerlab.org/code/acoustics_mmo_2023 [ABSTRACT FROM AUTHOR]