Graph Isomorphism Network: A Learning-based Workflow for Converter Inverse Design Problem

To surpass the limitation of conventional artificial intelligence-enabled automatic design methodology, this work proposes a novel workflow to implement the emerging graph-learning-based method called Graph Isomorphism Networks (GIN) for power converter automated design. A radio-frequency filter des...

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Bibliographic Details
Main Authors: Xue, Cheng, Li, Yuzhuo, Zargari, Faraz, Li, Yunwei
Format: Conference Proceeding
Language:English
Subjects:
Energy conversion
Filtering algorithms
Filtering theory
Graph Isomorphism Networks
Graph Neural Network
Graph Theory
Inverse Design problem
Knowledge engineering
Neural networks
Power Converter
Radio frequency
Training
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Description
Summary:To surpass the limitation of conventional artificial intelligence-enabled automatic design methodology, this work proposes a novel workflow to implement the emerging graph-learning-based method called Graph Isomorphism Networks (GIN) for power converter automated design. A radio-frequency filter design demo is provided to show the merits of bringing GIN into converter design, where similar tasks could take days if not weeks to achieve optimally by hand.
ISSN:2329-3748
DOI:10.1109/ECCE53617.2023.10362399