Modeling of Ship Maneuvering Motion Based on Neural Ordinary Differential Equations
Published in ISOPE International Ocean and Polar Engineering Conference, 2025
The safety of ship navigation and the optimization of maneuvering paths are closely dependent on the accurate prediction of maneuvering motions, which are typically governed as nonlinear dynamic models. This study proposes a Neural Ordinary Differential Equation (Neural ODE)-based modeling approach to improve the prediction of ship maneuvering behavior. In the framework, ship motion states and control signals are fed into a multi-layer neural network, which is then embedded within an ODE solver to model the system dynamics from a continuous-time perspective. This integration enables the model to capture the underlying dynamic characteristics of ship motion. The basic effectiveness of the proposed Neural ODE method is evaluated by the simulated data of the KRISO container ship under various scenarios, including turning circle, zigzag, and random rudder tests. The results demonstrate that the model has good generalization ability across different test conditions, confirming the fundamental applicability of the proposed method.
Recommended citation: Liu, S., & Wang, Z. (2025, June). Modeling of Ship Maneuvering Motion Based on Neural Ordinary Differential Equations. In ISOPE International Ocean and Polar Engineering Conference (pp. ISOPE-I). ISOPE.
Recommended citation: Liu, S., & Wang, Z. (2025, June). Modeling of Ship Maneuvering Motion Based on Neural Ordinary Differential Equations. In ISOPE International Ocean and Polar Engineering Conference (pp. ISOPE-I). ISOPE.
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