What does this research mean for the field?

The proposed machine learning approach effectively predicts ship resistance components while maintaining physical interpretability and generalizability across different hull shapes. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to improve predictions of ship resistance components using a combination of machine learning and physics-based methods.

February 17, 2026Open Access

Advanced Machine Learning for Ship Resistance Prediction

Key Points

The aim is to improve predictions of ship resistance components using a combination of machine learning and physics-based methods.
Integrated machine learning with traditional physics-based approaches.
Predicted resistance components such as frictional and wave resistance.
Maintained interpretability while modeling complex interactions across different hull shapes.
Achieved improved prediction accuracy of resistance components compared to traditional methods.
Enhanced understanding of nonlinear interactions in ship resistance.
Retained physical interpretability in model outputs.

Abstract

Ship resistance strongly influences fuel consumption and greenhouse gas emissions Traditional semi empiricalformulas (e g ITTC/Holtrop simplify complex physics and may miss nonlinear interactions across diverse hull shapesMachine learning can model nonlinear relationships, but purely data driven models often lack physical interpretabilityand may not generalize across different hull families Our approach combines physics with machine learning wepredict resistance components (e g frictional and wave and recombine them using physics based methods to maintaininterpretability and constraints

Advanced Machine Learning for Ship Resistance Prediction

Key Points

Abstract

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