Lettuce: PyTorch-based Lattice Boltzmann Framework
This work addresses the problem of bridging machine learning and computational fluid dynamics for researchers and engineers, though it is incremental as it builds on existing LBM and PyTorch capabilities.
The authors tackled the lack of integration between machine learning and the lattice Boltzmann method (LBM) by introducing Lettuce, a PyTorch-based framework that enables GPU-accelerated LBM simulations, rapid prototyping, and seamless combination with deep learning tools, demonstrated through a neural collision model trained on a shear layer and applied to decaying turbulence, and flow control in forced isotropic turbulence.
The lattice Boltzmann method (LBM) is an efficient simulation technique for computational fluid mechanics and beyond. It is based on a simple stream-and-collide algorithm on Cartesian grids, which is easily compatible with modern machine learning architectures. While it is becoming increasingly clear that deep learning can provide a decisive stimulus for classical simulation techniques, recent studies have not addressed possible connections between machine learning and LBM. Here, we introduce Lettuce, a PyTorch-based LBM code with a threefold aim. Lettuce enables GPU accelerated calculations with minimal source code, facilitates rapid prototyping of LBM models, and enables integrating LBM simulations with PyTorch's deep learning and automatic differentiation facility. As a proof of concept for combining machine learning with the LBM, a neural collision model is developed, trained on a doubly periodic shear layer and then transferred to a different flow, a decaying turbulence. We also exemplify the added benefit of PyTorch's automatic differentiation framework in flow control and optimization. To this end, the spectrum of a forced isotropic turbulence is maintained without further constraining the velocity field. The source code is freely available from https://github.com/lettucecfd/lettuce.