TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - nicht begutachtet (unreviewed)
A1  - Bedrunka, Mario Christopher
A1  - Wilde, Dominik
A1  - Kliemank, Martin
A1  - Reith, Dirk
A1  - Foysi, Holger
A1  - Krämer, Andreas
T1  - Lettuce: PyTorch-based Lattice Boltzmann Framework
N2  - 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.
KW  - Pytorch
KW  - Automatic Differentiation
KW  - Neural networks
KW  - Machine learning
KW  - Lattice Boltzmann Method Code
U6  - https://doi.org/10.48550/arXiv.2106.12929
DO  - https://doi.org/10.48550/arXiv.2106.12929
AX  - 2106.12929
PB  - arXiv
ER  -