Data-Driven Estimation of Ship Manoeuvring Hydrodynamic Derivatives using a Hybrid MARUS–PINN Framework with LLM-Guided Symbolic Discovery

作者：Paria Rezayan, Konstantinos Domdouzis, Yogang Singh · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.20634302 · 研究领域：Ship Hydrodynamics and Maneuverability、Maritime Navigation and Safety、Maritime Transport Emissions and Efficiency

Identification of hydrodynamic derivatives that govern ship manoeuvring behaviour for autonomous vessels such as unmanned surface vehicles (USVs) is essential for diverse maritime purposes including oceanographic research, environmental monitoring, and autonomous navigation. However, conventional hydrodynamic modelling approaches, such as classical manoeuvring models, experimental methods, and high-fidelity computational fluid dynamics (CFD), are often labour-intensive, costly, and time-consuming, and their estimated parameters may not fully capture real-world operating conditions due to nonlinearities, scale effects, and complex sea states. With the advent of machine learning, recent advances in Physics-Informed Neural Networks (PINNs) offer a promising alternative by embedding governing fluid-dynamic equations directly into the neural learning network, providing the advantages of data-driven learning while maintaining physical consistency. Hence, this thesis investigates a novel approach that combines Marine Robotics Unity Simulator (MARUS)–simulated manoeuvring data, PINN-based parameter learning, and symbolic discovery via Large Language Models (LLMs) to estimate hydrodynamic derivatives with improved robustness, reduced data requirements, and enhanced interpretability compared to conventional system identification methods, while promoting interoperability and standardization through its modular design. To evaluate this hybrid approach, the thesis addresses the following ...