A source-extended Lie-group algorithm derives Ward identities for elastic Burgulence, showing viscoelastic turbulence has weaker symmetry constraints than Navier-Stokes turbulence.
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Philosophical Transactions of the Royal Society of London
4 Pith papers cite this work, alongside 1,369 external citations. Polarity classification is still indexing.
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Introduces three-parameter quartic Lyapunov functions to verify global stability of 2D shear flows beyond energy method limits using symmetries and analytical refinements.
Three-dimensional three-temperature simulations of colliding supersonic plasma flows from irradiated CH mesh targets produce a persistent shocked turbulent mixing layer that evolves toward an isothermal state with anisotropic Reynolds stress and effective Reynolds number around 200.
SerpentFlow aligns large-scale wind patterns across GCM and observational domains then uses flow-matching to generate consistent fine-scale multivariate wind fields, outperforming standard bias correction in spatial coherence and robustness.
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Functional Renormalization for Elastic Burgulence
A source-extended Lie-group algorithm derives Ward identities for elastic Burgulence, showing viscoelastic turbulence has weaker symmetry constraints than Navier-Stokes turbulence.
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Quartic Lyapunov functions for global fluid stability
Introduces three-parameter quartic Lyapunov functions to verify global stability of 2D shear flows beyond energy method limits using symmetries and analytical refinements.
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Numerical simulations of shock-driven, supersonic turbulence in colliding three-temperature laboratory plasmas
Three-dimensional three-temperature simulations of colliding supersonic plasma flows from irradiated CH mesh targets produce a persistent shocked turbulent mixing layer that evolves toward an isothermal state with anisotropic Reynolds stress and effective Reynolds number around 200.
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Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
SerpentFlow aligns large-scale wind patterns across GCM and observational domains then uses flow-matching to generate consistent fine-scale multivariate wind fields, outperforming standard bias correction in spatial coherence and robustness.