Loubna Benabbou

Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents

Abdessamad El-Kabid

Redouane Lguensat

Alex Hern'andez-Garc'ia

Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanogr… (voir plus)aphy, high-resolution current data are critical for coastal management, environmental monitoring, and maritime safety. However, available satellite products, such as Copernicus data for sea water velocity at ~0.08 degrees spatial resolution and global ocean models, often lack the spatial granularity required for detailed local analyses. In this work, we (a) introduce a supervised deep learning framework based on neural operators for solving PDEs and providing arbitrary resolution solutions, and (b) propose downscaling models with an application to Copernicus ocean current data. Additionally, our method can model surrogate PDEs and predict solutions at arbitrary resolution, regardless of the input resolution. We evaluated our model on real-world Copernicus ocean current data and synthetic Navier-Stokes simulation datasets.

2025-07-24

ArXiv (prépublication)

arxiv.org

Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents

Abdessamad El-Kabid

Loubna Benabbou

Redouane Lguensat

Alex Hernandez-Garcia

Accurate modeling of physical systems governed by partial differential equations is a central challenge in scientific computing. In oceanogr… (voir plus)aphy, high-resolution current data are critical for coastal management, environmental monitoring, and maritime safety. However, available satellite products, such as Copernicus data for sea water velocity at ~0.08 degrees spatial resolution and global ocean models, often lack the spatial granularity required for detailed local analyses. In this work, we (a) introduce a supervised deep learning framework based on neural operators for solving PDEs and providing arbitrary resolution solutions, and (b) propose downscaling models with an application to Copernicus ocean current data. Additionally, our method can model surrogate PDEs and predict solutions at arbitrary resolution, regardless of the input resolution. We evaluated our model on real-world Copernicus ocean current data and synthetic Navier-Stokes simulation datasets.

2025-07-01

arXiv (publié)

doi.org

arxiv.org

Advancing Sustainable Maritime Transport: A Machine Learning Approach to Predict and Mitigate Underwater Radiated Noise from Ships

Soukaina Boujdi

Ayoub Atanane

Loubna Benabbou

Pierre Cauchy

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

doi.org

A Comparative Analysis of AI Models for Short-Term Solar Irradiance Forecasting

Saad Benbrahim

Abdelaziz Berrado

Hanane Dagdougui

Loubna Benabbou

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

doi.org

Enhancing Hybrid Model for Photovoltaic Power Prediction: A Case Study of Morocco

Samira Abousaid

Abdelaziz Berrado

Hanane Dagdougui

Loubna Benabbou

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

doi.org

Predicting greenhouse gas Emissions in Shipping: A Case Study Of Canada

Abdelhak EL AISSI

Loubna Benabbou

Abdelaziz Berrado

Stephane Carron

2025-04-07

Proceedings of the International Conference on Industrial Engineering and Operations Management (publié)

doi.org

A Decomposition-Based Framework for Large-Scale Multi-Period Log-Truck Routing and Scheduling: A Case Study in Canadian Forestry

Abdelhakim Abdellaoui

François Aubé

Loubna Benabbou

I. E. Hallaoui

Mouloud Amazouz

2025-01-01

IFAC-PapersOnLine (publié)

doi.org

Predicting Vessel Speed Over Ground: A Machine Learning Approach for Enhancing Maritime Transport

Ismail Bourzak

Loubna Benabbou

Sara El Mekkaoui

Abdelaziz Berrado

Stéphane Caron

2025-01-01

IFAC-PapersOnLine (publié)

doi.org

Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks

Adel Guitouni

Nabil Belacel

Loubna Benabbou

Belaid Moa

Munire Erman

Halim Abdul

Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidl… (voir plus)y evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study’s findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making.

2024-09-27

Scientific Reports (publié)

doi.org

Longitudinal bi-criteria framework for assessing national healthcare responses to pandemic outbreaks

Adel Guitouni

Nabil Belacel

Loubna Benabbou

Belaid Moa

Munire Erman

Halim Abdul

2024-09-27

Scientific Reports (publié)