A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.
Variational assimilation of meteorological observations with the adjoint vorticity equation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Divide-and-conquer modeling using scenario-specific techniques reaches a public score of 79.63 on the CTF-4-Science Lorenz benchmark.
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Reinforcement Learning, Optimal Control, and Bayesian Filtering in Data Assimilation
A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.
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Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark
Divide-and-conquer modeling using scenario-specific techniques reaches a public score of 79.63 on the CTF-4-Science Lorenz benchmark.