Physics-Informed Neural Networks for Robust System Identification of Ship Roll Dynamics With Noise Resilience

Abstract

Parameter identification in nonlinear offshore ship dynamics is crucial yet challenging, especially when measurement noise complicates the process. Differential models are particularly susceptible to large errors due to the discrete numerical differentiation of noisy data. To enhance noise resilience and achieve accurate parameter estimates, this article proposes the use of physics-informed neural networks (PINN) to identify ship roll dynamics. Constrained by physical principles, the PINN learns ship parameters with physical interpretability, employing automatic differentiation to circumvent the noise issues inherent in discrete differentiation. Leveraging the periodic nature of roll motion, a novel activation function is introduced to improve training efficiency. Robustness is validated through simulations of ship roll dynamics under regular and random wave excitations at various noise levels. Full-scale experiments conducted in open sea conditions confirm the practical effectiveness of the proposed approach in real-world scenarios.