Generative AI in Turbulence: Foundations, Applications, Validations and Benchmarks
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2507. 00719v3 Announce Type: replace-cross Abstract: Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy.
arXiv:2506. 20771v2 Announce Type: replace Abstract: We propose a latent score-based generative AI framework for learning stochastic, non-local closure models and constitutive laws in nonlinear dynamical systems of computational mechanics.
arXiv:2607. 13022v1 Announce Type: cross Abstract: Many nonlinear physical systems exhibit an initial transient phase in which perturbations grow before nonlinear interactions lead to a statistically steady state.
Many nonlinear physical systems exhibit an initial transient phase in which perturbations grow before nonlinear interactions lead to a statistically steady state. While this saturated regime is of primary interest, direct numerical simulations must resolve the full transient dynamics before reaching it, incurring significant computational cost.
The paper presents a turbulence‑aware framework for reconstructing high‑fidelity three‑dimensional flow fields from coarse‑grained data. It combines a multiscale neural operator with a state‑space model to enforce temporal consistency, incorporates Navier‑Stokes residuals as soft constraints, and uses a mixture‑of‑experts mechanism for unsupervised adaptation to unknown coarse‑graining. Experiments on canonical turbulent systems and atmospheric reanalysis data show reduced reconstruction errors and better preservation of multiscale spatial structure and coherent temporal evolution compared to existing learning‑based and classical baselines.
High-fidelity three-dimensional turbulent flow fields are crucial for scientific and engineering analyses, but direct numerical simulation or high-resolution measurements are costly. The paper introduces a turbulence-aware framework that couples a multiscale neural operator with a state space model and incorporates Navier-Stokes residuals as soft constraints to reconstruct time-resolved 3‑D flow fields from coarse-grained data. The method also uses a mixture-of-experts mechanism for unsupervised adaptation to unknown coarse-graining, and demonstrates reduced reconstruction errors and improved preservation of multiscale spatial structure and coherent temporal evolution on canonical turbulent systems and atmospheric reanalysis data.