Exploiting Generative Models for Performance Predictions of 3D Car Designs

IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–9

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 prediction accuracy. In sum, we find that geometric deep learning approaches offer powerful tools to support the engineering design process.

Authors 9

1. Sneha Saha
   Affiliation as printed

   Honda Research Institute Europe GmbH, Offenbach, Germany

2. Thiago Rios
   Affiliation as printed

   Honda Research Institute Europe GmbH, Offenbach, Germany

3. Leandro Lei Minku

   University of Birmingham

   Affiliation as printed

   CERCIA, School of Computer Science, University of Birmingham, Birmingham, UK

4. Bas van Stein Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS), Leiden, The Netherlands

5. Patricia Wollstadt
   Affiliation as printed

   Honda Research Institute Europe GmbH, Offenbach, Germany

6. Xin Yao

   Southern University of Science and Technology · University of Birmingham

   Affiliation as printed

   CERCIA, School of Computer Science, University of Birmingham, Birmingham, UK

   Southern University of Science and Technology, Shenzhen, China

7. Thomas Bäck Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS), Leiden, The Netherlands

8. Bernhard Sendhoff
   Affiliation as printed

   Honda Research Institute Europe GmbH, Offenbach, Germany

9. Stefan Menzel
   Affiliation as printed

   Honda Research Institute Europe GmbH, Offenbach, Germany