An offline-trained controller augments autoregressive diffusion models to perform fast, feed-forward data assimilation in chaotic spatiotemporal PDEs with order-of-magnitude speedups and improved accuracy over baselines.
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DATO and QMDA represent substantially different assimilation paradigms with distinct advantages and limitations in interpretability, robustness, and scalability.
This is an introductory review of the linear algebraic subproblems and contemporary solvers in variational data assimilation for geophysical applications.
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From Classical to Quantum-Mechanical Data Assimilation: A Comparison between DATO and QMDA
DATO and QMDA represent substantially different assimilation paradigms with distinct advantages and limitations in interpretability, robustness, and scalability.