Physics-Informed Graph Neural Networks for Water Distribution Systems
Ashraf MI, Strotherm J, Hermes L, Hammer B (2024)
In: THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20. Palo alto: Assoc Advancement Artificial Intelligence.
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Einrichtung
Abstract / Bemerkung
Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. As 70% of the world's population will likely live in urban environments in 2050, efficient simulation and planning tools for WDS play a crucial role in reaching UN's sustainable developmental goal (SDG) 6 - "Clean water and sanitation for all". In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model, for hydraulic state estimation in WDS. Using a recursive approach, our model only needs a few graph convolutional neural network (GCN) layers and employs an innovative algorithm based on message passing. Unlike conventional machine learning tasks, the model uses hydraulic principles to infer two additional hydraulic state features in the process of reconstructing the available ground truth feature in an unsupervised manner. To the best of our knowledge, this is the first DL approach to emulate the popular hydraulic simulator EPANET, utilizing no additional information. Like most DL models and unlike the hydraulic simulator, our model demonstrates vastly faster emulation times that do not increase drastically with the size of the WDS. Moreover, we achieve high accuracy on the ground truth and very similar results compared to the hydraulic simulator as demonstrated through experiments on five real-world WDS datasets.
Erscheinungsjahr
2024
Titel des Konferenzbandes
THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20
Konferenz
38th Annual AAAI Conference on Artificial Intelligence 2024
Konferenzort
Vancouver
Konferenzdatum
20.02.2024 – 27.02.2024
ISSN
2159-5399
eISSN
2374-3468
Page URI
https://pub.uni-bielefeld.de/record/2988175
Zitieren
Ashraf MI, Strotherm J, Hermes L, Hammer B. Physics-Informed Graph Neural Networks for Water Distribution Systems. In: THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20. Palo alto: Assoc Advancement Artificial Intelligence; 2024.
Ashraf, M. I., Strotherm, J., Hermes, L., & Hammer, B. (2024). Physics-Informed Graph Neural Networks for Water Distribution Systems. THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20 Palo alto: Assoc Advancement Artificial Intelligence.
Ashraf, Muhammad Inaam, Strotherm, Janine, Hermes, Luca, and Hammer, Barbara. 2024. “Physics-Informed Graph Neural Networks for Water Distribution Systems”. In THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20. Palo alto: Assoc Advancement Artificial Intelligence.
Ashraf, M. I., Strotherm, J., Hermes, L., and Hammer, B. (2024). “Physics-Informed Graph Neural Networks for Water Distribution Systems” in THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20 (Palo alto: Assoc Advancement Artificial Intelligence).
Ashraf, M.I., et al., 2024. Physics-Informed Graph Neural Networks for Water Distribution Systems. In THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20. Palo alto: Assoc Advancement Artificial Intelligence.
M.I. Ashraf, et al., “Physics-Informed Graph Neural Networks for Water Distribution Systems”, THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20, Palo alto: Assoc Advancement Artificial Intelligence, 2024.
Ashraf, M.I., Strotherm, J., Hermes, L., Hammer, B.: Physics-Informed Graph Neural Networks for Water Distribution Systems. THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20. Assoc Advancement Artificial Intelligence, Palo alto (2024).
Ashraf, Muhammad Inaam, Strotherm, Janine, Hermes, Luca, and Hammer, Barbara. “Physics-Informed Graph Neural Networks for Water Distribution Systems”. THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 20. Palo alto: Assoc Advancement Artificial Intelligence, 2024.
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