Browsing by Author "Celik, Ugurcan"

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    A certifiable AI-based braking control framework for landing using scientific machine learning
    (IEEE, 2024-09-29) Uzun, Mevlut; Celik, Ugurcan; Guner, Guney; Ozdemir, Orhan; Inalhan, Gokhan
    This paper proposes an AI-based braking control system for aircraft during landing. Utilizing scientific machine learning, we train an agent to apply the most effective braking strategy under various landing conditions. This approach ensures physically consistent outputs by grounding the algorithm in the principles of landing physics. Our results demonstrate that the aircraft can successfully decelerate without skidding across all runway conditions and landing speeds. Additionally, the algorithm maintains performance and safety even when brake performance degradation and initial yaw angles are introduced. This robustness is crucial for the certification of AI in safety-critical systems, as the proposed framework provides a reliable and effective solution.
  • Thumbnail Image
    ItemOpen Access
    Scientific machine learning based pursuit-evasion strategy in unmanned surface vessel defense tactics
    (IEEE, 2024-09-29) Celik, Ugurcan; Uzun, Mevlut; Inalhan, Gokhan; Woods, Mike
    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.