Jocelyn Ahmed Mazari — accepted AI-conference papers

2025

ML4CFD Competition: Results and Retrospective Analysis

Mouadh Yagoubi, David Danan, Milad Leyli-abadi, Jocelyn Ahmed Mazari, Jean-Patrick Brunet, Abbas Kabalan +12

NeurIPS 2025poster

The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computational fluid dynamics (CFD). Yet, persistent challenges in accuracy, generalization, and physical consistency hinder the…

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2022

AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier–Stokes Solutions

Florent Bonnet, Jocelyn Ahmed Mazari, Paola Cinnella, patrick gallinari

NeurIPS 2022accept

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier–Stokes equations. However, despite the fast-growing field of data-dr…

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