Exploiting Generative Models for Performance Predictions of 3D Car Designs

Sneha Saha1; Thiago Rios1; Leandro Minku2; Bas Vas Stein3; Patricia Wollstadt,1; Xin Yao2,4; Thomas Back3; Bernhard Sendhoff1; Stefan Menzel1

10.1109/SSCI50451.2021.9660034

2021

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

978-1-7281-9049-5

2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021)

1-9

DEC 05-07, 2021

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

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.

representation learning 3D point clouds autoencoder variational autoencoder regression analysis

其他

英语

CPCI-S ; EI

European Union[766186]

Computer Science ; Engineering ; Operations Research & Management Science ; Mathematics

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science ; Mathematics, Applied

WOS:000824464300214

20221011761212

Deep learning ; Forecasting ; Regression analysis ; Signal encoding ; Stochastic models ; Vehicle performance

Ergonomics and Human Factors Engineering:461.4 ; Automobiles:662.1 ; Heavy Duty Motor Vehicles:663.1 ; Information Theory and Signal Processing:716.1 ; Control Systems:731.1 ; Probability Theory:922.1 ; Mathematical Statistics:922.2 ; Systems Science:961

Web of Science

1.Honda Research Institute Europe GmbH
2.University of Birmingham
3.Leiden Institute of Advanced Computer Science (LIACS)
4.Department of Computer Science and Engineering, Southern University of Science and Technology

Sneha Saha,Thiago Rios,Leandro Minku,et al. Exploiting Generative Models for Performance Predictions of 3D Car Designs[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:1-9.