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Variational Autoencoders for Dimensionality Reduction of Automotive Vibroacoustic Models

Journal Article
ISSN: 2641-9637, e-ISSN: 2641-9645
Published June 15, 2022 by SAE International in United States
Variational Autoencoders for Dimensionality Reduction of Automotive Vibroacoustic Models
Citation: Schmid, J., Hildenbrand, A., Gurbuz, C., Luegmair, M. et al., "Variational Autoencoders for Dimensionality Reduction of Automotive Vibroacoustic Models," SAE Int. J. Adv. & Curr. Prac. in Mobility 5(2):830-838, 2023,
Language: English


In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models.
In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system. Autoencoders are neural networks consisting of an encoder and a decoder network and they are trained in order to learn the identity mapping between a reduced approximation and the initial input while enforcing a dimensionality reduction in the latent space. This allows decoding the hidden data generating structure behind the data and enables an interpretation based on the latent variables, which is extremely valuable in the engineering design process. The performance of the VAE approach is compared to a conventional principal component analysis. Finally, the trained VAE is used as a deep generative model and it is investigated to which extent the pre-trained decoder network can be used to generate new artificial realizations at low costs. These artificially generated samples can then be used to enhance the training data basis for other neural network approaches or data-driven surrogate models.