Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons

In this paper, we train turbulence models based on convolutional neural networks. These learned turbulence models improve under-resolved low-resolution solutions to the incompressible Navier-Stokes equations at simulation time. Our study involves the development of a differentiable numerical solver that supports the propagation of optimisation gradients through multiple solver steps. The significance of this property is demonstrated by the superior stability and accuracy of those models that unroll more solver steps during training. Furthermore, we introduce loss terms based on turbulence physics that further improve the model accuracy. This approach is applied to three two-dimensional turbulence flow scenarios, a homogeneous decaying turbulence case, a temporally evolving mixing layer and a spatially evolving mixing layer. Our models achieve significant improvements of long-term a posteriori statistics when compared with no-model simulations, without requiring these statistics to be directly included in the learning targets. At inference time, our proposed method also gains substantial performance improvements over similarly accurate, purely numerical methods.

Automated summary

In this paper, turbulence models trained by convolutional neural networks are proposed to improve low-resolution solutions of the incompressible Navier-Stokes equations. A differentiable numerical solver is developed to support optimization gradients propagation through multiple solver steps, and the importance of this property is demonstrated. Loss terms based on turbulence physics are introduced to enhance model accuracy. The proposed method achieves significant improvements in long-term a posteriori statistics compared to no-model simulations, and also outperforms purely numerical methods in terms of performance.