Physics-informed machine learning models for ship speed prediction

• Published in: Expert Systems with Applications
• Main Authors: Lang, Xiao, Wu, Da, Mao, Wengang
• Language: unknown
• Published: 1481
• Subjects: Transport Systems and Logistics; Astronomy; Astrophysics and Cosmology; Marine Engineering; Computer Science; Ship speed over ground; Physics-informed neural networks; Machine learning; XGBoost; Full-scale measurements; ETA; Grey-box model; Eta; Pacific; North Atlantic
• Online Access: https://doi.org/10.1016/j.eswa.2023.121877; https://research.chalmers.se/en/publication/537824

This paper proposes a novel physics-informed machine learning method to build grey-box model (GBM) predicting ship speed for ocean crossing ships. In this method, the expected ship speed in calm water is first modeled by the physics-informed neural networks (PINNs) based on speed-power model tests....