arXiv:2606.11691v1 Announce Type: new Abstract: Latent diffusion and flow matching have emerged as leading approaches for synthetic turbulence generation, yet they systematically under-represent dissipation-range amplitudes. We introduce a latent flow matching framework with a spectrally regularized compression stage that directly targets this failure mode. On a 256^2 DNS dataset at Re_f \approx 2250, replacing an MSE-trained VAE with a zone-weighted log-spectral objective raises deep-dissipation retained spectral power from 25% to 94% in reconstruction and from 20% to 79% in unconditional gen
The rapid advancement of latent diffusion and flow matching methods has exposed limitations in generating realistic turbulence, prompting immediate research into improved regularization techniques.
Accurate simulation of turbulence is critical for numerous scientific and engineering applications, and improved AI generation methods can accelerate research and development in these fields.
The ability to synthetically generate turbulence with high fidelity, particularly in the critical dissipation range, has significantly improved, potentially reducing computational costs and improving model accuracy.
- · AI researchers
- · Fluid dynamics engineers
- · Aerospace industry
- · Climate modeling
- · Traditional CFD methods (comparatively)
More accurate and faster simulations of complex fluid phenomena become possible.
Accelerated design and optimization cycles for aircraft, hydrodynamic systems, and energy generation technologies.
Potential for new discoveries in areas like plasma physics and atmospheric science due to enhanced simulation capabilities.
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Read at arXiv cs.LG