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On Diverse System-Level Design Using Manifold Learning and Partial Simulated Annealing

Published online by Cambridge University Press:  26 May 2022

A. Cobb ,
A. Roy ,
D. Elenius ,
K. Koneripalli  and
S. Jha
A. Cobb
Affiliation:
SRI International, United States of America
A. Roy
Affiliation:
SRI International, United States of America
D. Elenius
Affiliation:
SRI International, United States of America
K. Koneripalli
Affiliation:
SRI International, United States of America
S. Jha*
Affiliation:
SRI International, United States of America
*
susmit.jha@sri.com

Abstract

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The goal in system-level design is to generate a diverse set of high-performing design configurations that allow trade-offs across different objectives and avoid early concretization. We use deep generative models to learn a manifold of the valid design space, followed by Monte Carlo sampling to explore and optimize design over the learned manifold, producing a diverse set of optimal designs. We demonstrate the efficacy of our proposed approach on the design of an SAE race vehicle and propeller.

Keywords

artificial intelligence (AI)engineering designcyber-physical systems
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1541 - 1548
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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