Celik, UgurcanUzun, MevlutInalhan, GokhanWoods, Mike2025-01-072025-01-072024-09-29Celik 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, USA2155-7195https://doi.org/10.1109/dasc62030.2024.10749622https://dspace.lib.cranfield.ac.uk/handle/1826/23330In 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/46 Information and Computing Sciences4602 Artificial IntelligenceMachine Learning and Artificial IntelligenceNetworking and Information Technology R&D (NITRD)Conference paper2155-7209559316