A neural operator-conditioned diffusion model reconstructs ocean surface states with high-wavenumber fidelity from 99% to 99.9% sparse observations, outperforming standard UNET and FNO baselines.
NA and LJ conducted the research and developed the computational models with equal contributions
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Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
A neural operator-conditioned diffusion model reconstructs ocean surface states with high-wavenumber fidelity from 99% to 99.9% sparse observations, outperforming standard UNET and FNO baselines.