mlsanityData to 14 Oct 2026

Training-Free Data Assimilation with GenCast

arXiv 2509.18811 ↗First posted 23 Sep 2025cs.LG physics.ao-phcited by 5

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Abstract (arXiv)

Data assimilation is widely used in many disciplines such as meteorology, oceanography, and robotics to estimate the state of a dynamical system from noisy observations. In this work, we propose a lightweight and general method to perform data assimilation using diffusion models pre-trained for emulating dynamical systems. Our method builds on particle filters, a class of data assimilation algorithms, and does not require any further training. As a guiding example throughout this work, we illustrate our methodology on GenCast, a diffusion-based model that generates global ensemble weather forecasts.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 37 references that are in the map. First posted on arXiv 23 Sep 2025.

Builds on

16 of 37 references.

References · 16

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
  6. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,416
  7. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 938
  8. 2020Learning to Simulate Complex Physics with Graph Networkscited by 880
  9. 2016Accelerating Eulerian Fluid Simulation With Convolutional Networkscited by 130
  10. 2023DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecastingcited by 109
  11. 2024DiffDA: a Diffusion Model for Weather-scale Data Assimilationcited by 61
  12. 2024Learning Diffusion Priors from Observations by Expectation Maximizationcited by 61
4 more
  1. 2024On conditional diffusion models for PDE simulationscited by 60
  2. 2025Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulationcited by 19
  3. 2025Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusioncited by 11
  4. 2025Align-DA: Align Score-based Atmospheric Data Assimilation with Multiple Preferencescited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolantscited by 5
  2. 2026Training-Free Bayesian Filtering with Generative Emulatorscited by 4
  3. 2026Accurate and Efficient Hybrid-Ensemble Atmospheric Data Assimilation in Latent Space with Uncertainty Quantificationcited by 1
  4. 2026MIRA: A Score for Conditional Distribution Accuracy and Model Comparisoncited by 1
  5. 2026EnJoi: Ensemble Joint Score Filter for Generative Data Assimilationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026SURGE: Approximation and Training Free Particle Filter for Diffusion Surrogate
  2. 2023Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic model
  3. 2025FlowDAS: A Stochastic Interpolant-based Framework for Data Assimilation
  4. 2024Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales
  5. 2026DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants
  6. 2026Closed-form conditional diffusion models for data assimilation
  7. 2026Generative models for simulation based filtering: Formulations and Empirical Comparisons
  8. 2026A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants