Scientific Machine Learning Based Pursuit-Evasion Strategy in Unmanned Surface Vessel Defense Tactics

Ugurcan Celik, Mevlut Uzun, Gokhan Inalhan, Mike Woods

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

Özet

In this work we develop an AI-aided tactics generator for uncrewed surface vessels (USVs) for protection of critical national infrastructure and maritime assets in face of surface vehicle attacks. Our scientific machine learning (SciML) based methodology incorporates physical principles into the learning process, enhancing the model's ability to generalize and perform accurately in scenarios not encountered during training. This innovation addresses a critical gap in existing AI applications for maritime defense: the ability to operate effectively in novel or changing conditions without the need for retraining.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıDASC 2024 - Digital Avionics Systems Conference, Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9798350349610
DOI'lar
Yayın durumuYayınlandı - 2024
Harici olarak yayınlandıEvet
Etkinlik43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024 - San Diego, United States
Süre: 29 Eyl 20243 Eki 2024

Yayın serisi

AdıAIAA/IEEE Digital Avionics Systems Conference - Proceedings
ISSN (Basılı)2155-7195
ISSN (Elektronik)2155-7209

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???event.eventtypes.event.conference???43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024
Ülke/BölgeUnited States
ŞehirSan Diego
Periyot29/09/243/10/24

Bibliyografik not

Publisher Copyright:
© 2024 IEEE.

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