machine learning 2equivariance 1fluid dynamics 1large eddy simulation 1neural networks 1nonlocal neural networks 1rotational equivariance 1subgrid-scale modeling 1turbulence 1turbulent flow 1
From the 2 of 4 linked papers with an AI index.
Showing physics.flu-dynShow all
3 papers · 1 filter
physics.flu-dyn2026
Rotational equivariance and locality in data-driven subgrid-scale closures
Ryley McConkey, Julia Balla, Elyssa Hofgard +2
The paper evaluates how enforcing rotational equivariance in data‑driven subgrid‑scale models for large‑eddy simulation impacts accuracy, parameter efficiency, and generalization,…
physics.flu-dyn2026
Turbulence teaches equivariance to neural networks
Ryley McConkey, Julia Balla, Jeremiah Bailey +5
The paper investigates how the rotational symmetries of turbulent flows influence neural network learning, showing that models respecting these symmetries generalize better and tha…
physics.flu-dyn2026
The Closure Challenge: a benchmark task for machine learning in turbulence modelling
Ryley McConkey, Tyler Buchanan, Tess Smidt +3
We introduce a field-wide benchmark challenge for machine learning in Reynolds-averaged Navier-Stokes (RANS) turbulence modelling. Though open-source datasets exist for training da…