From the 2 of 7 linked papers with an AI index.
7 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…
Implicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution
Julia Balla, Jeremiah Bailey, Ali Backour +4
The immense computational cost of simulating turbulence has motivated the use of machine learning approaches for super-resolving turbulent flows. A central challenge is ensuring th…
Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings
Ryley McConkey, Nikhila Kalia, Eugene Yee +1
Within the context of machine learning-based closure mappings for RANS turbulence modelling, physical realizability is often enforced using ad-hoc postprocessing of the predicted a…
Kolmogorov-Arnold Networks for Turbulence Anisotropy Mapping
Nikhila Kalia, Ryley McConkey, Eugene Yee +1
This study evaluates the generalization performance and representation efficiency (parsimony) of a previously introduced Tensor Basis Kolmogorov-Arnold Network (TBKAN) architecture…