AI Meets Turbulence Modeling: Complex Systems Perspective

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ID: 333346
2026
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Abstract
Abstract Despite widespread enthusiasm, applying artificial intelligence and machine learning (AI/ML) to turbulence modeling has yielded only modest progress, primarily due to a lack of situation-aware deployment. Data-driven Reynolds-averaged Navier–Stokes (ML-RANS) closures frequently fail to generalize across diverse flow regimes, while scale-resolving simulations (SRS) remain computationally prohibitive. As a result, ML-RANS is often misapplied outside its domain of validity, and ML-SRS is unnecessarily deployed on flows fully amenable to RANS. To guide optimal AI/ML deployment, we propose a turbulence taxonomy map parameterized by degrees of freedom (Re) and the degree of non-equilibrium (SK/ε). By applying Weaver’s well-recognized complexity framework, we classify turbulence into distinct physical regimes. This categorization identifies where local ML-RANS surrogates can generalize, clarifies where explicit scale resolution is physically mandatory, and highlights where physics-informed neural networks (PINNs) are most effective. Ultimately, this perspective establishes a physics-grounded framework for the flow-aware, successful deployment of AI/ML in turbulence.
Reference Key
openalex_W7221778469 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sharath S. Girimaji
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Journal national science review
Year 2026
DOI
10.1093/nsr/nwag654
URL
https://doi.org/10.1093/nsr/nwag654
Keywords Keywords not found

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