Closed-form conditional diffusion models for data assimilation
We propose closed-form conditional diffusion models for data assimilation. Diffusion models use data to learn the score function (defined as the gradient of the log-probability density of a data distribution), allowing them to generate new samples from the data distribution by reversing a noise injection process. While it is common to train neural networks to approximate the score function, we leverage the analytical tractability of the score function to assimilate the states of a system with measurements. To enable the efficient evaluation of the score function, we use kernel density estimation to model the joint distribution of the states and their corresponding measurements. The propos...
| Authors | Brianna Binder, Agnimitra Dasgupta, Assad Oberai |
|---|---|
| Primary category | stat.ML |
| Published | 22 March 2026 |
| Last updated at source | 31 March 2026 |
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