AI RESEARCH
DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants
arXiv CS.LG
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ArXi:2512.00252v3 Announce Type: replace-cross Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observations to estimate latent system states. Classical high-dimensional DA methods, such as the ensemble Kalman filter, rely on Gaussian approximations that are violated for complex dynamics or observation operators. To address this limitation, we