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Machine Learning Based Simulation for Design Space Exploration

Published online by Cambridge University Press:  26 May 2022

O. Bleisinger,
C. Malek and
S. Holbach
O. Bleisinger*
Affiliation:
Fraunhofer IESE, Germany
C. Malek
Affiliation:
Fraunhofer IESE, Germany
S. Holbach
Affiliation:
BorgWarner Turbo Systems Engineering GmbH, Germany
*
oliver.bleisinger@iese.fraunhofer.de

Abstract

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Design of software in the automotive domain often involves simulation to allow early software parametrization. Modeling complex systems or components impacted by the software in an analytical way can be time-consuming, require domain knowledge and executing the analytical models can result in high computational effort. In specific applications, these challenges can be overcome by applying machine learning based simulation. This contribution presents results of a case study in which powertrain components are modeled data-driven with artificial neural networks to support design space exploration

Keywords

artificial intelligence (AI)simulation-based designdata-driven design

Information

Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022, pp. 1521 - 1530
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

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