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.
Deep Neural Networks for Data-Driven
2 Pith papers cite this work. Polarity classification is still indexing.
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Linear theory predicts regimes for deformable surfaces in turbulence where the interface is enslaved by flow or shows intrinsic dynamics; simulations of air-water and rubber match predictions without wave turbulence.
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Free surfaces in turbulence -- A unified framework from water surfaces to elastic solids
Linear theory predicts regimes for deformable surfaces in turbulence where the interface is enslaved by flow or shows intrinsic dynamics; simulations of air-water and rubber match predictions without wave turbulence.