Authors:
Andrija Grbavac
1
;
Martin Angerbauer
1
;
Michael Grill
1
and
André Kulzer
2
Affiliations:
1
Research Institute for Automotive Engineering and Powertrain Systems Stuttgart (FKFS), University of Stuttgart, Pfaffenwaldring 12, Stuttgart, Germany
;
2
Institute of Automotive Engineering (IFS), University of Stuttgart, Stuttgart, Germany
Keyword(s):
Graph Neural Networks, Physical System Models, Application of AI.
Abstract:
Efficiently categorizing physical system models is crucial for data science applications in scientific and engineering realms, facilitating insightful analysis, control, and optimization. While current methods, often relying on Convolutional Neural Networks (CNNs), effectively handle spatial dependencies in image data, they struggle with intricate relationships inherent in physical system models. Our research introduces a novel approach employing Graph Neural Networks (GNNs) to enhance categorization. GNNs excel in modeling complex relational structures, making them apt for analyzing interconnected components within physical systems represented as graphs. Leveraging GNNs, our methodology treats entities as system components and edges as their arrangements, effectively learning and exploiting inherent dependencies and interactions. The proposed GNN-based approach outperforms CNN-based methods across a dataset of 55 physical system models, eliminating limitations observed in CNN approa
ches. The results underscore GNNs’ ability to discern subtle interdependencies and capture non-local patterns, enhancing the accuracy and robustness of model categorization in a data science framework. This research contributes to advancing model categorization, emphasizing the application of data science for understanding and controlling complex physical systems. The innovative use of GNNs opens new avenues for revolutionizing the categorization of intricate physical system models in scientific and engineering domains.
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