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.


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    Title :

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


    Contributors:


    Publication date :

    2024-09-29


    Size :

    3794943 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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