Core Concepts
The authors leverage differentiable programming techniques to learn highly effective and versatile subgrid-scale (SGS) models for two-dimensional turbulent flow using physics-inspired deep learning architectures.
Abstract
The content discusses the use of differentiable programming techniques to learn subgrid-scale (SGS) turbulence models for large eddy simulations (LES) of two-dimensional turbulent flow. The key highlights are:
The authors develop an end-to-end differentiable solver that combines deep learning models with traditional numerical methods to learn SGS closures. This "differentiable turbulence" approach allows backpropagation of the a-posteriori error to the model parameters.
Several neural network architectures are evaluated, including multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), Fourier neural operators (FNOs), and a hybrid CNN+FNO model. The analysis shows that capturing small-scale non-local features is critical for effective SGS modeling, while large-scale features can improve pointwise accuracy.
The velocity gradient tensor is decomposed into isotropic, deviatoric, and anti-symmetric components, which are then mapped to the SGS stress through the neural network.
The differentiable physics approach is shown to outperform offline, a-priori learning of the SGS stress. The hybrid solver-in-the-loop approach offers a balance between computational efficiency, accuracy, and generalization.
The learned models are evaluated on a variety of flow configurations, including higher and lower Reynolds numbers and different forcing conditions. The results demonstrate the ability of the models to generalize to unseen flow regimes.
Stats
The following sentences contain key metrics or figures:
The velocity gradient tensor on the LES grid can be mapped directly to the SGS stress via decomposition of the inputs and outputs into isotropic, deviatoric, and anti-symmetric components.
We see that the model can generalize to a variety of flow configurations, including higher and lower Reynolds numbers and different forcing conditions.
Quotes
"The concept of "reverse-mode differentiation" in CFD simulations has existed for decades and is analogous to solving the adjoint problem."
"Differentiable simulations have been integrated with ML as "solver-in-the-loop" approaches particularly for correcting and improving the accuracy of coarse-grained or unresolved simulations, which offers a balance between computational cost and physics-embedding."