Physics-informed Data-driven Approach for Ship Docking Prediction

Abstract

Accurate ship motion predictions play a vital role in supporting the decision-making process onboard. Generally, the ship dynamics are described by either a deterministic model derived from hydrodynamic principles or a black-box model learned from the observations. However, there are always cases in real life where the physics information is insufficient to develop a complete model, and the data quantity is also limited so that a data-driven model is away from expectation. For this obstacle, we propose a physics-data cooperative modeling approach based on a rough ship numerical model and a few operational data to enhance the model quality. The prior knowledge leveraged by the ship’s numerical model is integrated into the neural network as informative inputs, and the informed neural network calibrates the bias between model outcomes and actual states in principle. The proposed approach is validated in the real docking operation of a research vessel. Comparisons with both the purely hydrodynamic model and the data-driven model without physics informed are conducted. The results convinced that the physicsdata hybrid way yields a more accurate model with relaxed data requirements and less learning consumption.