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Deep learning models turbulence closures for large eddy simulations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new deep learning approach for turbulence closure modeling in large eddy simulations (LES). This method uses a nudging technique, treating direct numerical simulation (DNS) data as sparse observations to train the model. This allows for a-priori training of closures, enabling the model to learn necessary forcing for accurate statistics while maintaining long-term stability without requiring backpropagation through the LES solver. AI

IMPACT Introduces a more stable and computationally efficient method for turbulence closure modeling in simulations.

RANK_REASON Academic paper detailing a novel deep learning approach for turbulence modeling.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning models turbulence closures for large eddy simulations

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Academic paper detailing a novel deep learning approach for turbulence modeling.
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ashwin Suriyanarayanan, Melissa Adrian, Dibyajyoti Chakraborty, Romit Maulik ·

    Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics

    arXiv:2604.23874v1 Announce Type: cross Abstract: Deep learning approaches have shown remarkable promise in turbulence closure modeling for large eddy simulations (LES). The differentiable physics paradigm uses the so-called a-posteriori approach for learning by embedding a neura…

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