Computer-assisted global regularity across nonlinear families of three-dimensional periodic Navier-Stokes flows

• URL: http://arxiv.org/abs/2609.16157v1
• Date: Mon, 14 Sep 2026 18:02:50 GMT
• Title: Computer-assisted global regularity across nonlinear families of three-dimensional periodic Navier-Stokes flows
• Abstract summary: I develop a computer-assisted framework that establishes global regularity for three-dimensional periodic Navier-Stokes flows.<n>The method retains the complete nonlinear residual before spectral truncation and controls the evolution until decay guarantees regularity for all subsequent times.<n>An ensemble of 4,096 configurations, supplemented by 1,600 refinement trajectories and public turbulence data, connects the mathematical observables to spectral transfer and vortex geometry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Numerical simulations reveal how vortices stretch and transfer energy, but establishing smooth evolution requires bounds that remain valid beyond the simulated resolution. Here I develop a computer-assisted framework that establishes global regularity for continuous families of three-dimensional periodic Navier-Stokes flows. Its central construction combines finite reference trajectories with a common error bound that covers an interval of centre fields and infinitely many smooth perturbation modes. The method retains the complete nonlinear residual before spectral truncation and controls the evolution until viscous decay guarantees regularity for all subsequent times. Applications to cyclic-shear, Arnold-Beltrami-Childress and three-component Taylor-Green fields yield explicit perturbation radii and include initial conditions outside the direct Fourier-Wiener smallness criterion. A parameter-uniform extension covers a connected family of non-Beltrami Taylor-Green centres without repeating the proof for individual parameter values. An ensemble of 4,096 configurations, supplemented by 1,600 refinement trajectories and public turbulence data, connects the mathematical observables to spectral transfer and vortex geometry. Matched neural-operator experiments show that physics-informed training improves physical prediction, while also revealing that these gains do not necessarily improve the discovery of proof-limiting initial conditions. Together, these results provide a reusable method for establishing regularity across prescribed flow families and a quantitative setting for evaluating how learned predictions can assist rigorous computation.

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