3 citations · 4 across the 5 of their papers we have counts for
5 papers
NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design
Mouadh Yagoubi, David Danan, Milad Leyli-abadi +8
The integration of machine learning (ML) techniques for addressing intricate physics problems is increasingly recognized as a promising avenue for expediting simulations. However,…
Data-driven turbulence modeling
Paola Cinnella
This chapter provides an introduction to data-driven techniques for the development and calibration of closure models for the Reynolds-Averaged Navier--Stokes (RANS) equations. RAN…
Enhancing non-intrusive Reduced Order Models with space-dependent aggregation methods
Anna Ivagnes, Niccolò Tonicello, Paola Cinnella +1
In this manuscript, we combine non-intrusive reduced order models (ROMs) with space-dependent aggregation techniques to build a mixed-ROM. The prediction of the mixed formulation i…
A priori tests of turbulence models for compressible flows
Sciacovelli L., Cannici A., Passiatore D. +1
A priori tests of turbulence models for the compressible Reynolds-Averaged Navier--Stokes (RANS) are performed by using Direct Numerical Simulations (DNS) data of zero-pressure-gra…
Space-dependent turbulence model aggregation using machine learning
Maximilien de Zordo-Banliat, Grégory Dergham, Xavier Merle +1
In this article, we propose a data-driven methodology for combining the solutions of a set of competing turbulence models. The individual model predictions are linearly combined fo…