Hugging Face Trending Papers August 17, 2026

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

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arXiv Machine Learning

Aug 18

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.

By Samarasimha Reddy Chittamuru, Ayhan Akinturk, Allison Kennedy, Joshua Barnes, Matthew Hamilton

arXiv Machine Learning

Jun 17

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

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.

By R. F. Su\'arez, J. C. Berndt, M. Abdel-Maksoud

benchmarks

arXiv Machine Learning

Jun 5

Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data

arXiv:2606. 06156v1 Announce Type: new Abstract: Machine learning-based predictive emissions monitoring systems offer a practical alternative to direct emissions measurement, but their deployment across gas turbine fleets is challenging when emissions labels are available for only a small subset of assets.

By Rebecca Potts, Aiden Durrant, Rick Hackney, Georgios Leontidis

arXiv Machine Learning

Jul 22

Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

arXiv:2607. 19054v1 Announce Type: new Abstract: In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations.

By Hannes Nilsson, Rafael Basso, Bal\'azs Kulcs\'ar, Morteza Haghir Chehreghani

arXiv Machine Learning

Jul 14

Inter-Stop Energy Prediction and Causal Driver Quantification for Dual-Source Trolleybuses via a Time-Aware Tabular Deep Learning Architecture

arXiv:2607. 11349v1 Announce Type: cross Abstract: Dual-source trolleybuses alternate between overhead catenary supply and on-board battery operation, creating energy-use patterns driven by route attributes, high-frequency trajectories, and hourly weather.

By Wentao Zeng (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China, School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan, China), Zijian Huang (School of Artificial Intelligence, South China Normal University, Guangzhou, China), Yiming Bie (School of Transportation, Jilin University, Changchun, China), Jiabin Wu (School of Management, Foshan University, Foshan, China a School of Management, Foshan University, Foshan, China), Jun Gong (Department of Civil Engineering, The University of Hong Kong, Hong Kong, China)

benchmarks

arXiv Machine Learning

Jun 4

RIDE: An Open Dataset and Benchmark for Train Delay Prediction

arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.

By Cl\'ement Elliker, Mathis Le Bail, Cl\'ement Mantoux, Jesse Read, Sonia Vanier

benchmarks data-engineering

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Related stories

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

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

Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data

Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data

Inter-Stop Energy Prediction and Causal Driver Quantification for Dual-Source Trolleybuses via a Time-Aware Tabular Deep Learning Architecture

RIDE: An Open Dataset and Benchmark for Train Delay Prediction