Opportunities for machine learning in scientific discovery

no code implementations • 7 May 2024 • Ricardo Vinuesa, Jean Rabault, Hossein Azizpour, Stefan Bauer, Bingni W. Brunton, Arne Elofsson, Elias Jarlebring, Hedvig Kjellstrom, Stefano Markidis, David Marlevi, Paola Cinnella, Steven L. Brunton

Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields.

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

3 code implementations • 15 Dec 2022 • Florent Bonnet, Ahmed Jocelyn Mazari, Paola Cinnella, Patrick Gallinari

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive.

Quantification of Model Uncertainty in RANS Simulations: A Review

1 code implementation • 27 Jun 2018 • Heng Xiao, Paola Cinnella

In computational fluid dynamics simulations of industrial flows, models based on the Reynolds-averaged Navier--Stokes (RANS) equations are expected to play an important role in decades to come.

Fluid Dynamics Computational Physics