Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic model
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
This paper presents an ensemble data assimilation method using the pseudo ensembles generated by denoising diffusion probabilistic model. Since the model is trained against noisy and sparse observation data, this model can produce divergent ensembles close to observations. Thanks to the variance in generated ensembles, our proposed method displays better performance than the well-established ensemble data assimilation method when the simulation model is imperfect.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 10 of its 26 references that are in the map. First posted on arXiv 13 Aug 2023.
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10 of 26 references.
References · 10
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- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2021Latent Space Data Assimilation by using Deep Learningcited by 29
- 2022Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systemscited by 12
- 2020Analog ensemble data assimilation and a method for constructing analogs with variational autoencoderscited by 11
- 2021Super-resolution data assimilationcited by 8
- 2021Machine Learning Techniques to Construct Patched Analog Ensembles for Data Assimilationcited by 7
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