Portrait de Loubna Benabbou

Loubna Benabbou

Membre affilié
loubna.benabbou@umontreal.ca
UQAR
Sujets de recherche
Apprentissage profond
Optimisation
Théorie de l'apprentissage automatique
Site web
Google Scholar

Étudiants actuels

Abdessamad EL KABID
Collaborateur·rice alumni - UdeM
Superviseur⋅e principal⋅e :
Yoshua Bengio
Github
Amine RAZIG
Stagiaire de recherche - UdeM
Superviseur⋅e principal⋅e :
Yoshua Bengio
Site web

Publications

Multiscale Neural PDE Surrogates for Prediction and Downscaling: Application to Ocean Currents
Abdessamad El-Kabid
Loubna Benabbou
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
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
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)
doi.org
Deep learning based vessel arrivals monitoring via autoregressive statistical control charts
Sara El Mekkaoui
Ghait Boukachab
Loubna Benabbou
Abdelaziz Berrado
2024-07-01
WMU Journal of Maritime Affairs (JoMA) (publié)
doi.org
Deep learning based vessel arrivals monitoring via autoregressive statistical control charts
Sara El Mekkaoui
Ghait Boukachab
Loubna Benabbou
Abdelaziz Berrado
2024-07-01
WMU Journal of Maritime Affairs (JoMA) (publié)
doi.org
Towards a framework selection for assessing the performance of photovoltaic solar power plants: criteria determination
Meryam Chafiq
Loubna Benabbou
Hanane Dagdougui
Ismail Belhaj
Abdelali Djdiaa
Hicham Bouzekri
Abdelaziz Berrado
Mastery of Key Performance Indicators (KPIs) in the realm of photovoltaic solar power plants is pivotal for evaluating their effectiveness a… (voir plus)nd fine-tuning their operational efficiency. The assessment of these plants' performance has con-sistently stood as a focal point in scientific research. Nevertheless, the investigation into the process of selecting a framework for classifying KPIs, particularly through their categorization based on criteria, sub-criteria, or aspects, has been relatively limited in research. This article addresses this gap by conducting a comprehensive literature review on various KPIs and, drawing upon both literature and practical experience, formulating a set of criteria to serve as the foundation for a Multi-Criteria Decision Analysis (MCDA) method. This intricate taxonomic framework enhances the understanding of infrastructure performance for stakeholders in the solar industry. By streamlining decision-making, it simplifies the selection of KPIs tailored to specific requirements, thus mitigating the complexity arising from the abundance of KPIs in the literature. As a result, decision-makers can make well-informed choices regarding the monitoring and evaluation framework that best suits the performance goals of their solar plant.
2024-05-16
2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (publié)
doi.org
Towards a connection between the capacitated vehicle routing problem and the constrained centroid-based clustering
Abdelhakim Abdellaoui
Loubna Benabbou
Issmail ElHallaoui
Efficiently solving a vehicle routing problem (VRP) in a practical runtime is a critical challenge for delivery management companies. This p… (voir plus)aper explores both a theoretical and experimental connection between the Capacitated Vehicle Routing Problem (CVRP) and the Constrained Centroid-Based Clustering (CCBC). Reducing a CVRP to a CCBC is a synonym for a transition from an exponential to a polynomial complexity using commonly known algorithms for clustering, i.e K-means. At the beginning, we conduct an exploratory analysis to highlight the existence of such a relationship between the two problems through illustrative small-size examples and simultaneously deduce some mathematically-related formulations and properties. On a second level, the paper proposes a CCBC based approach endowed with some enhancements. The proposed framework consists of three stages. At the first step, a constrained centroid-based clustering algorithm generates feasible clusters of customers. This methodology incorporates three enhancement tools to achieve near-optimal clusters, namely: a multi-start procedure for initial centroids, a customer assignment metric, and a self-adjustment mechanism for choosing the number of clusters. At the second step, a traveling salesman problem (T SP) solver is used to optimize the order of customers within each cluster. Finally, we introduce a process relying on routes cutting and relinking procedure, which calls upon solving a linear and integer programming model to further improve the obtained routes. This step is inspired by the ruin&recreate algorithm. This approach is an extension of the classical cluster-first, route-second method and provides near-optimal solutions on well-known benchmark instances in terms of solution quality and computational runtime, offering a milestone in solving VRP.
2024-03-01
arXiv (publié)
doi.org
arxiv.org