Learning to reconstruct Germano Differential Filtered Fields with Convolutional Neural Networks for Large Eddy Simulation of Plasma Turbulence
Abstract
We present a novel method which combines the invertibility of the differential filter of Germano, with convolutional networks to accelerate plasma turbulence simulations. Rather than learning the mapping from coarse-grid LES fields to fine-grid DNS, the proposed method learns the mapping from coarse LES fields to the differential-filtered DNS fields. We then use the inverse of Germano's filter to reconstruct the fine-scale details and hence calculate exactly the nonlinear terms in the governing equations and subgrid-scale terms in the LES equations. The method is tested on the 2D Hasegawa-Wakatani (HW) and modified Hasegawa-Wakatani (mHW) plasma turbulence models at 2562 and 5122 DNS resolutions using 2, 4 and 8 times smaller LES grids in each direction. We compare the model with DNS results on energy, enstrophy, spectra, probability distributions, poloidal and radial fluxes and computational cost. Validation extends to 1000 simulation time units for HW and 100 for mHW, whose faster dynamics lead toward laminarization. For the 2562 cases, the finer LES meshes preserve several integral and spectral features, whereas the coarsest mesh yields unphysical behavior. In contrast, for the 5122 cases, even the coarsest LES mesh preserves the turbulent character of the flow and maintains acceptable statistics for both the HW and mHW models. Moreover, we show how our methodology is superior to traditional algebraic numerical LES models, which traditionally fail to capture important design features. Finally, benchmarks show an MPI task-time reductions of 4.82--49.45, with a maximum wall-clock speedup of 1.55× when compared to full CPU socket.
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BibTeX
@misc{castagna2026learning,
title = {{Learning to reconstruct Germano Differential Filtered Fields with Convolutional Neural Networks for Large Eddy Simulation of Plasma Turbulence}},
author = {Jony Castagna and Josh Williams},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.20204},
eprint = {2610.20204},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.20204},
}
RIS
TY - UNPB
TI - Learning to reconstruct Germano Differential Filtered Fields with Convolutional Neural Networks for Large Eddy Simulation of Plasma Turbulence
AU - Jony Castagna
AU - Josh Williams
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.20204
UR - https://arxiv.org/abs/2610.20204
AB - We present a novel method which combines the invertibility of the differential filter of Germano, with convolutional networks to accelerate plasma turbulence simulations. Rather than learning the mapping from coarse-grid LES fields to fine-grid DNS, the proposed method learns the mapping from coarse LES fields to the differential-filtered DNS fields. We then use the inverse of Germano's filter to reconstruct the fine-scale details and hence calculate exactly the nonlinear terms in the governing equations and subgrid-scale terms in the LES equations. The method is tested on the 2D Hasegawa-Wakatani (HW) and modified Hasegawa-Wakatani (mHW) plasma turbulence models at 256^2 and 512^2 DNS resolutions using 2, 4 and 8 times smaller LES grids in each direction. We compare the model with DNS results on energy, enstrophy, spectra, probability distributions, poloidal and radial fluxes and computational cost. Validation extends to 1000 simulation time units for HW and 100 for mHW, whose faster dynamics lead toward laminarization. For the 256^2 cases, the finer LES meshes preserve several integral and spectral features, whereas the coarsest mesh yields unphysical behavior. In contrast, for the 512^2 cases, even the coarsest LES mesh preserves the turbulent character of the flow and maintains acceptable statistics for both the HW and mHW models. Moreover, we show how our methodology is superior to traditional algebraic numerical LES models, which traditionally fail to capture important design features. Finally, benchmarks show an MPI task-time reductions of 4.82--49.45, with a maximum wall-clock speedup of 1.55× when compared to full CPU socket.
ER -