Data-Driven Model Categorization: Advancing Physical Systems Analysis Through Graph Neural Networks

Andrija Grbavac, Martin Angerbauer, Michael Grill, André Kulzer

2024

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 approaches. 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.

Paper Citation

in Harvard Style

Grbavac A., Angerbauer M., Grill M. and Kulzer A. (2024). Data-Driven Model Categorization: Advancing Physical Systems Analysis Through Graph Neural Networks. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 277-284. DOI: 10.5220/0012747100003756

in Bibtex Style

@conference{data24,
author={Andrija Grbavac and Martin Angerbauer and Michael Grill and André Kulzer},
title={Data-Driven Model Categorization: Advancing Physical Systems Analysis Through Graph Neural Networks},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={277-284},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012747100003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Data-Driven Model Categorization: Advancing Physical Systems Analysis Through Graph Neural Networks
SN - 978-989-758-707-8
AU - Grbavac A.
AU - Angerbauer M.
AU - Grill M.
AU - Kulzer A.
PY - 2024
SP - 277
EP - 284
DO - 10.5220/0012747100003756
PB - SciTePress