mlsanityData to 14 Oct 2026

Physics-Preserving AI-Accelerated Simulations of Plasma Turbulence

arXiv 2309.16400 ↗First posted 28 Sep 2023physics.comp-ph cs.AI physics.plasm-phcited by 4

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Abstract (arXiv)

Turbulence in fluids, gases, and plasmas remains an open problem of both practical and fundamental importance. Its irreducible complexity usually cannot be tackled computationally in a brute-force style. Here, we combine Large Eddy Simulation (LES) techniques with Machine Learning (ML) to retain only the largest dynamics explicitly, while small-scale dynamics are described by an ML-based sub-grid-scale model. Applying this novel approach to self-driven plasma turbulence allows us to remove large parts of the inertial range, reducing the computational effort by about three orders of magnitude, while retaining the statistical physical properties of the turbulent system.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 71 references that are in the map. First posted on arXiv 28 Sep 2023.

Builds on

17 of 71 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2022A ConvNet for the 2020scited by 3,579
  4. 2019The frontier of simulation-based inferencecited by 502
  5. 2021Machine learning accelerated computational fluid dynamicscited by 471
  6. 2019Machine Learning for Fluid Mechanicscited by 385
  7. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
  8. 2020Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solverscited by 205
  9. 2019Towards Physics-informed Deep Learning for Turbulent Flow Predictioncited by 204
  10. 2018Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flowscited by 150
  11. 2021Enhancing Computational Fluid Dynamics with Machine Learningcited by 138
  12. 2020Learning to Control PDEs with Differentiable Physicscited by 132
5 more
  1. 2021Learned Coarse Models for Efficient Turbulence Simulationcited by 91
  2. 2021Scientific multi-agent reinforcement learning for wall-models of turbulent flowscited by 52
  3. 2019Poisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian meshcited by 22
  4. 2020AI-assisted super-resolution cosmological simulationscited by 15
  5. 2021Using neural networks to solve the 2D Poisson equation for electric field computation in plasma fluid simulationscited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systemscited by 2
  2. 2026Optimal-Transport-Guided Functional Flow Matching for Turbulent Field Generation in Hilbert Spacecited by 1
  3. 2026Deep Wave Network for Modeling Multi-Scale Physical Dynamicscited by 0
  4. 2026Generative Diffusion Surrogates with Analytical Variance Schedulecited by 0