MASF redesigns the forward diffusion process to align with measurements, yielding a theoretically grounded likelihood score and up to 28.2x speedup on O(10^5)-dimensional Kolmogorov flow under sparse and nonlinear observation operators.
Ensemble kalman filter in latent space using a variational autoencoder pair.arXiv preprint arXiv:2502.12987
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Rethinking Forward Processes for Score-Based Nonlinear Data Assimilation in High Dimensions
MASF redesigns the forward diffusion process to align with measurements, yielding a theoretically grounded likelihood score and up to 28.2x speedup on O(10^5)-dimensional Kolmogorov flow under sparse and nonlinear observation operators.