A new computer-assisted framework has been developed to establish global regularity for three-dimensional periodic Navier-Stokes flows. This method combines finite reference trajectories with a common error bound, retaining the full nonlinear residual before spectral truncation and controlling evolution until viscous decay guarantees regularity. Applications to various flow fields have yielded explicit perturbation radii and included initial conditions beyond standard criteria, with a parameter-uniform extension covering a family of non-Beltrami Taylor-Green centers. The framework also connects mathematical observables to spectral transfer and vortex geometry through extensive configuration data and matched neural-operator experiments, demonstrating how physics-informed training can aid rigorous computation. AI
IMPACT Physics-informed AI training can enhance discovery of proof-limiting conditions in complex simulations.
RANK_REASON Academic paper detailing a new computational framework for fluid dynamics. [lever_c_demoted from research: ic=1 ai=0.7]
- alphaXiv
- Arnold-Beltrami-Childress
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- Influence Flower
- Jose Luis Lima De Jesus Silva
- Navier–Stokes equations
- ScienceCast
- Taylor Green
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