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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Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.
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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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Towards bridging the gap between data-driven and theoretical turbulence closures in stratified flows
Explores theoretical and data-driven closures for ocean mesoscale eddies and examines their connections using analytical and data-driven methods.