Comparison of data-driven symmetry-preserving closure models for large-eddy simulation

TL;DR

This paper compares symmetry-preserving data-driven closure models for large-eddy simulation, demonstrating that enforcing symmetries leads to more physically consistent turbulence modeling compared to unconstrained neural networks.

Contribution

It evaluates and compares tensor-basis neural networks, group-convolutional neural networks, and unconstrained networks for LES closures, highlighting the benefits of symmetry enforcement.

Findings

Symmetry-preserving models outperform classical closures in accuracy.

Enforcing symmetries yields more physically consistent velocity-gradient statistics.

Unconstrained networks have similar prediction accuracy but less physical consistency.

Abstract

Symmetries are fundamental to both turbulence and differential equations. The large-eddy simulation (LES) equations inherit these symmetries provided the LES closure respects them. Classical LES closures based on eddy viscosity or scale similarity preserve many of the original symmetries by design. Recently, data-driven neural network closures have been applied to LES to improve accuracy, but stability and generalizability remain challenges, as symmetries are not automatically enforced. In this work, we compare approaches for constructing symmetry-preserving data-driven LES closures, including tensor-basis neural networks (TBNNs) and group-convolutional neural networks, alongside unconstrained convolutional networks. All three data-driven closures outperform classical models in both the functional sense (producing the right amount of dissipation) and the structural sense (stress tensor prediction). While unconstrained networks achieve comparable prediction accuracy, symmetry-preserving models produce more physically consistent velocity-gradient statistics, suggesting that enforcing symmetries improves the quality of the learned closure beyond what aggregate error metrics such as relative tensor prediction errors capture.