This project proposes the development of a Physics-Informed Neural Network (PINN) framework for vessel trajectory prediction using Automatic Identification System (AIS) data collected from Canadian maritime regions. The approach integrates discretized kinematic motion equations—specifically Euler, Heun, and midpoint finite difference approximations—into the training of deep learning models to enforce physical consistency. By combining data-driven architectures […]
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