From the 2 of 4 linked papers with an AI index.
4 papers
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,…
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…
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…
A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer
Abigail Bodner, Dhruv Balwada, Laure Zanna
Parameterizations of O(1-10)km submesoscale flows in General Circulation Models (GCMs) represent the effects of unresolved vertical buoyancy fluxes in the ocean mixed layer. These…