Scientific machine learning based pursuit-evasion strategy in unmanned surface vessel defense tactics

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dc.contributor.authorCelik, Ugurcan
dc.contributor.authorUzun, Mevlut
dc.contributor.authorInalhan, Gokhan
dc.contributor.authorWoods, Mike
dc.date.accessioned2025-01-07T16:42:07Z
dc.date.available2025-01-07T16:42:07Z
dc.date.freetoread2025-01-07
dc.date.issued2024-09-29
dc.date.pubOnline2024-11-15
dc.description.abstractIn 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.
dc.description.conferencename2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)
dc.description.sponsorshipEngineering and Physical Sciences Research Council
dc.description.sponsorshipUgurcan Celik is co-funded by the EPSRC and the BAE Systems under 220124 numbered Industrial CASE award:Towards AI-driven Intelligent Decision Making in Warfare.
dc.identifier.citationCelik U, Uzun M, Inalhan G, Woods M. (2024) Scientific machine learning based pursuit-evasion strategy in unmanned surface vessel defense tactics. In: 2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC), 29 September 2024 - 3 October 2024, San Diego, CA, USA
dc.identifier.eissn2155-7209
dc.identifier.elementsID559316
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/dasc62030.2024.10749622
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23330
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10749622
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.titleScientific machine learning based pursuit-evasion strategy in unmanned surface vessel defense tactics
dc.typeConference paper
dcterms.coverageSan Deigo, CA. USA
dcterms.dateAccepted2024-03-22
dcterms.temporal.endDate3 Oct 2024
dcterms.temporal.startDate29 Sep 2024

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