On the importance of stochasticity in closures of turbulence
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908848711794688 |
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| author | Freitas, André Biferale, Luca Desbrun, Mathieu Eyink, Gregory Mailybaev, Alexei A. Um, Kiwon |
| author_facet | Freitas, André Biferale, Luca Desbrun, Mathieu Eyink, Gregory Mailybaev, Alexei A. Um, Kiwon |
| contents | Deterministic closures for coarse-grained turbulence models help reproduce mean statistics, but often fail to capture the finite-time growth of uncertainty. Using the framework of shell models as a quantitative multi-scale testbed, we compare fully resolved simulations with large-eddy simulations using either stochastic or deterministic subgrid closures. While in the fully resolved system a single microscopic perturbation is rapidly amplified by strongly chaotic dynamics, truncation produces a strong delay and suppression of variance growth when uncertainty is introduced through initial condition perturbations only. We show that a data-driven Langevin-type stochastic closure restores the correct timing and magnitude of variance growth across scales, demonstrating that sustained stochasticity is essential for predictability in reduced turbulent dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19875 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On the importance of stochasticity in closures of turbulence Freitas, André Biferale, Luca Desbrun, Mathieu Eyink, Gregory Mailybaev, Alexei A. Um, Kiwon Fluid Dynamics Chaotic Dynamics Computational Physics Deterministic closures for coarse-grained turbulence models help reproduce mean statistics, but often fail to capture the finite-time growth of uncertainty. Using the framework of shell models as a quantitative multi-scale testbed, we compare fully resolved simulations with large-eddy simulations using either stochastic or deterministic subgrid closures. While in the fully resolved system a single microscopic perturbation is rapidly amplified by strongly chaotic dynamics, truncation produces a strong delay and suppression of variance growth when uncertainty is introduced through initial condition perturbations only. We show that a data-driven Langevin-type stochastic closure restores the correct timing and magnitude of variance growth across scales, demonstrating that sustained stochasticity is essential for predictability in reduced turbulent dynamics. |
| title | On the importance of stochasticity in closures of turbulence |
| topic | Fluid Dynamics Chaotic Dynamics Computational Physics |
| url | https://arxiv.org/abs/2602.19875 |