Power-law tails in turbulent magnetic field PDFs arise from intermittent Poisson-distributed shocks convolved with a lognormal core, with tail asymmetry determined by the ratio of fast to slow MHD shocks.
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8 Pith papers cite this work, alongside 4,935 external citations. Polarity classification is still indexing.
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Spectrally regularized compression in latent flow matching raises retained deep-dissipation spectral power from 20% to 79% in generated turbulence on a 256^2 DNS dataset at Re_f ≈ 2250.
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
Computational searches maximizing LPS integrals and L3 norms in 3D periodic Navier-Stokes flows found no evidence of singularity formation, but quantified close approaches and transient growth.
IRIS observations of polar spicules reveal multiscale Alfvénic velocity fluctuations with a k_perp^{-1.43} spectrum, intermittency, and spatial coherence consistent with developing turbulence.
An a posteriori framework implemented in PyMHD estimates numerical dissipation in Alfvénic, dynamo, and MRI-driven MHD turbulence, showing it has distinct spectral and anisotropic properties from physical dissipation.
New ergodicity-based ACF estimator and integral timescale methodology for solar wind turbulence claimed to eliminate artificial interval-length dependence.
Anisotropic Gaussian primitives compress 3D Taylor–Green turbulence at 1e3–1e4× while recovering more intermediate- and high-wavenumber content than isotropic kernels.
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The dynamical origin of the magnetic field distributions in compressible turbulence
Power-law tails in turbulent magnetic field PDFs arise from intermittent Poisson-distributed shocks convolved with a lognormal core, with tail asymmetry determined by the ratio of fast to slow MHD shocks.
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Spectrally Regularized Latent Flow Matching for Turbulence Generation
Spectrally regularized compression in latent flow matching raises retained deep-dissipation spectral power from 20% to 79% in generated turbulence on a 256^2 DNS dataset at Re_f ≈ 2250.
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A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
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The Ladyzhenskaya-Prodi-Serrin Conditions and the Search for Extreme Behavior in 3D Navier-Stokes Flows
Computational searches maximizing LPS integrals and L3 norms in 3D periodic Navier-Stokes flows found no evidence of singularity formation, but quantified close approaches and transient growth.
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Spatially Coherent and Intermittent Alfv\'enic Fluctuations in Solar Polar Spicules
IRIS observations of polar spicules reveal multiscale Alfvénic velocity fluctuations with a k_perp^{-1.43} spectrum, intermittency, and spatial coherence consistent with developing turbulence.
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Characterization of Numerical Dissipation in Simulations of Magnetohydrodynamic Turbulence
An a posteriori framework implemented in PyMHD estimates numerical dissipation in Alfvénic, dynamo, and MRI-driven MHD turbulence, showing it has distinct spectral and anisotropic properties from physical dissipation.
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The integral and correlation scales of solar wind turbulence
New ergodicity-based ACF estimator and integral timescale methodology for solar wind turbulence claimed to eliminate artificial interval-length dependence.
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Gaussian Field Representations for Turbulent Flow: Compression, Scale Separation, and Physical Fidelity
Anisotropic Gaussian primitives compress 3D Taylor–Green turbulence at 1e3–1e4× while recovering more intermediate- and high-wavenumber content than isotropic kernels.