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Assessing Machine Learnability of Image and Graph Representations for Drone Performance Prediction

Published online by Cambridge University Press:  26 May 2022

B. Song ,
C. McComb  and
F. Ahmed
B. Song*
Affiliation:
Massachusetts Institute of Technology, United States of America
C. McComb
Affiliation:
Carnegie Mellon University, United States of America
F. Ahmed
Affiliation:
Massachusetts Institute of Technology, United States of America
*
binyangs@mit.edu

Abstract

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Deep learning (DL) from various representations have succeeded in many fields. However, we know little about the machine learnability of distinct design representations when using DL to predict design performance. This paper proposes a graph representation for designs and compares it to the common image representation. We employ graph neural networks (GNNs) and convolutional neural networks (CNNs) respectively to learn them to predict drone performance. GCNs outperform CNNs by 2.6-8.1% in predictive validity. We argue that graph learning is a powerful and generalizable method for such tasks.

Keywords

engineering designartificial intelligence (AI)design evaluationdesign representationgraph convolutional networks
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1777 - 1786
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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