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Lettuce: PyTorch-Based Lattice Boltzmann Framework

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High Performance Computing (ISC High Performance 2021)

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Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Siegen, Paul-Bonatz-Straße 9-11, 57076, Siegen-Weidenau, Germany

    Mario Christopher Bedrunka, Dominik Wilde & Holger Foysi

  2. Institute of Technology, Resource and Energy-efficient Engineering (TREE), Bonn-Rhein-Sieg University of Applied Sciences, Grantham-Allee 20, 53757, Sankt Augustin, Germany

    Mario Christopher Bedrunka, Dominik Wilde, Martin Kliemank & Dirk Reith

  3. Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany

    Dirk Reith

  4. Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195, Berlin, Germany

    Andreas Krämer

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  1. Mario Christopher Bedrunka
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  2. Dominik Wilde
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  6. Andreas Krämer
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Correspondence to Andreas Krämer .

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Editors and Affiliations

  1. University of Tennessee at Knoxville, Knowville, TN, USA

    Heike Jagode

  2. Karlsruhe Institute of Technology, Karlsruhe, Baden-Württemberg, Germany

    Hartwig Anzt

  3. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Hatem Ltaief

  4. University of Tennessee System, Knoxville, TN, USA

    Piotr Luszczek

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Bedrunka, M.C., Wilde, D., Kliemank, M., Reith, D., Foysi, H., Krämer, A. (2021). Lettuce: PyTorch-Based Lattice Boltzmann Framework. In: Jagode, H., Anzt, H., Ltaief, H., Luszczek, P. (eds) High Performance Computing. ISC High Performance 2021. Lecture Notes in Computer Science(), vol 12761. Springer, Cham. https://doi.org/10.1007/978-3-030-90539-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-90539-2_3

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