@inproceedings {pub4796,
	title = {Exploiting Generative Models for Performance Predictions of 3D Car Designs},
	author = {Sneha Saha AND Thiago de Jesus de Araujo Rios AND Leandro L. Minku AND Bas van Stein AND Patricia Wollstadt AND Xin Yao AND Thomas B{\"a}ck AND Bernhard Sendhoff AND Stefan Menzel},
	year = {2021},
	abstract = {In automotive digital development, engineers utilize multiple virtual prototyping tools to design and assess the performance of 3D shapes. However, accurate performance simulations
are computationally expensive and time-consuming, which may be prohibitive for design optimization tasks. To address this challenge, we envision a 3D design assistance system for design
exploration with performance assessment in the automotive domain. Recent advances in deep learning methods for learning geometric data are a promising step towards realizing such
systems. Deep learning-based (variational) autoencoder models have been used for learning and compressing 3D data allowing engineers to generate low-dimensional representations of
3D designs. Finding representations in a data-driven fashion results in representations that are agnostic to downstream tasks performed on these representations and are believed to capture
relevant design features. In this paper, we evaluate whether such data-driven representations contain relevant information about the input data and whether representations are meaningful in
performance prediction tasks for the input data. We use machine learning-based surrogate models to predict the performances of car shapes based on the low-dimensional representation learned
by 3D point cloud (variational) autoencoders. Furthermore, we exploit the stochastic nature of the representation learned by variational autoencoders to augment the training data for our
surrogate models, since the limited amount of data is usually a challenge for surrogate modeling in engineering. We demonstrate that augmenting training with generated shapes improves pre-
diction accuracy. In sum, we find that geometric deep learning approaches offer powerful tools to support the engineering design process.},
	publisher = {IEEE},
	booktitle = {IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2021)}
}