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 dil | İngilizce |
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Ana bilgisayar yayını başlığı | DASC 2024 - Digital Avionics Systems Conference, Proceedings |
Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Elektronik) | 9798350349610 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2024 |
Harici olarak yayınlandı | Evet |
Etkinlik | 43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024 - San Diego, United States Süre: 29 Eyl 2024 → 3 Eki 2024 |
Yayın serisi
Adı | AIAA/IEEE Digital Avionics Systems Conference - Proceedings |
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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 |
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Ülke/Bölge | United States |
Şehir | San Diego |
Periyot | 29/09/24 → 3/10/24 |
Bibliyografik not
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