arXiv Machine Learning June 17, 2026 By R. F. Su\'arez, J. C. Berndt, M. Abdel-Maksoud

Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study

Read the original on arXiv Machine Learning →

arXiv:2606. 17121v1 Announce Type: cross Abstract: This study investigates supervised machine learning techniques for identifying ship hydrodynamic coefficients from CFD-generated data from free-running simulations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

Jun 16

ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics

arXiv:2606. 15356v1 Announce Type: cross Abstract: Accurate prediction of hydrodynamic performance is central to ship design, yet high-fidelity computational fluid dynamics remains prohibitively expensive for large-scale parametric exploration.

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arXiv Machine Learning

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A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data

arXiv:2606. 19711v1 Announce Type: cross Abstract: Field-based modeling from onboard measurements can produce autonomous underwater vehicle (AUV) maneuvering models that reflect real operating characteristics.

By Aobo Wang, Aifei Xia, Zihao Wang, Lizhu Hao

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arXiv Machine Learning

1d ago

Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1\,Hz Operational Data, CCGS \textit{Sir Wilfrid Laurier}

arXiv:2608. 16833v1 Announce Type: new Abstract: Ship fuel consumption (SFC) prediction supports vessel operation optimisation, emissions estimation, and decision support systems (DSS) for sustainable maritime transportation.

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arXiv Machine Learning

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arXiv Machine Learning

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EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting

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arXiv Machine Learning

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An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications

arXiv:2607. 15077v1 Announce Type: new Abstract: Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known.

By Yao Cheng Li, Ana Larra\~naga, Steven L. Brunton, Urban Fasel

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ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics

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