The 17th INTERNATIONAL DESIGN CONFERENCE
DESIGN 2022
The 17th INTERNATIONAL DESIGN CONFERENCE - May 23-26, 2022 - Hotel Croatia, Cavtat, CAVTAT - DUBROVNIK, Croatia
Knowledge Base Repository
In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.
Assessing Machine Learnability of Image and Graph Representations for Drone Performance Prediction
Binyang Song (1), Christopher Mccomb (2), Faez Ahmed (1)
Type:
Year:
2022
Editor:
Mario Štorga, Stanko Škec, Tomislav Martinec, Dorian Marjanović
Author:
Series:
DESIGN
Institution:
1: Massachusetts Institute of Technology, United States of America; 2: Carnegie Mellon University, United States of America
Section:
Artificial Intelligence and Data-Driven Design
Page(s):
1777-1786
DOI number:
ISSN:
2732-527X (Online)
Abstract:
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:
