Data Assimilation

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Overview

Data assimilation estimates the evolving state of a system by combining imperfect observations with forecasts from a physical or learned model. Kalman and ensemble filters, particle filters, and variational methods are established approaches; newer work also uses latent-space models, neural operators, and generative methods such as diffusion and flow matching. These methods support applications including weather prediction, fluid reconstruction, ocean modeling, and subsurface characterization, often when observations are sparse or irregular. A central challenge is balancing computational cost and scalability with accurate uncertainty estimates and physically plausible results, especially for nonlinear systems and non-Gaussian posteriors.

Latest papers

CardsList

58 papers9 papers in the last four weeks, up 29% on the four weeks before. 0.1% of all new papers.

  1. Batched NN 4D-Var: a performant hybrid model framework for the Integrated Forecasting System

    Oct 23, 2026Alban Farchi, Marcin Chrust, Massimo Bonavita +3Weather ForecastingData Assimilation

    ECMWF Bonn, Germany · ECMWF Reading, United Kingdom · CEREA, ENPC, EDF R&D Institut Polytechnique de Paris Île–de–France, France

  2. Observation Assimilation Transformer: Probabilistic Weather Analysis from Sparse Station Observations

    Oct 20, 2026Christian Donner, Michele Cattaneo, Maxim Samarin +4Data AssimilationProbabilistic Forecasting

    Swiss Data Science Center, Eidgenössische Technische Hochschule Zürich (ETHZ) · Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland

  3. Accounting for model error in ensemble variational data assimilation through learned generative dynamics

    Oct 12, 2026Marc Bocquet, Tobias S. Finn, Sibo ChengData AssimilationEnsemble Kalman Filtering

    CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris, Île-de-France, France

  4. LD-EnFF: Latent-Dynamics Ensemble Flow Filtering for Data Assimilation with Sparse Observations

    Oct 2, 2026Ziyu Tian, Kaichen Shen, Wenbo Hao +3Data AssimilationBayesian Filtering

    School of Mathematics, Georgia Institute of Technology · School of Computational Science and Engineering, Georgia Institute of Technology

  5. A Unified Framework for Bayesian Data Assimilation with Generative Models and Observation Interpolants

    Oct 2, 2026Nikolaj T. Mücke, Benjamin SanderseData AssimilationPosterior Sampling

    Scientific Computing, Centrum Wiskunde & Informatica · Faculty of Civil Engineering and Geosciences, Delft University of Technology · Centre for Analysis, Scientific Computing and Applications, Eindhoven University of Technology

  6. DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants

    Oct 2, 2026Erik Wikingsson, Martin Andrae, Tomas Landelius +1Data AssimilationFlow-Based Generative Modeling

    Linköping University, Sweden · Swedish Meteorological and Hydrological Institute

  7. Benchmarking Generative Models for Near-Surface Data Assimilation on Real Station Observations

    Sep 30, 2026Ruizhe Huang, Qidong Yang, Jonathan Giezendanner +1Flow MatchingData Assimilation

    Massachusetts Institute of Technology, Cambridge, MA, USA

  8. Parameterization method of reservoir properties for ensemble-based data assimilation using intermediate latent space of StyleGAN

    Sep 30, 2026Marcio A. Sampaio, Paulo H. Ranazzi, Martin J. BluntData AssimilationGenerative Modeling

    Departamento de Engenharia de Minas e de Petróleo, Escola Politécnica, Universidade de São Paulo, 05508-030, SP, Brasil. · Department of Earth Science and Engineering, Imperial College London, South Kensington, London, UK

  9. EnJoi: Ensemble Joint Score Filter for Generative Data Assimilation

    Sep 28, 2026Julien Moreau, Marc LelargeData AssimilationDiffusion Models

    Inria Paris, DI ENS PSL Research University Renault Group

  10. AECSF: Adaptive Ensemble Conditional Score Filtering for High-Dimensional Nonlinear Data Assimilation

    Sep 26, 2026Yangwen Zhang, Shiwei Ni, Xiaoping Zhang +2Data AssimilationBayesian Filtering

    School of Mathematical Sciences, Tongji University, Shanghai 200092, China · Department of Information and Computational Sciences, Wuhan University, Wuhan 430072, China · Department of Mathematics, University of South Carolina, Columbia, South Carolina 29208, USA

  11. Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces

    Sep 24, 2026Yang Xu, Dibyajyoti Chakraborty, Haiwen Guan +2Bayesian Inverse ProblemsData Assimilation

    School of Mechanical Engineering, Purdue University, West Lafayette, Indiana, USA · College of Information Sciences and Technology, The Pennsylvania State University, University Park, Pennsylvania, USA

  12. Improving Ensemble Filters with Flow Matching

    Sep 23, 2026Haoyuan Chen, Alexandre ThiéryData AssimilationEnsemble Kalman Filtering

    Department of Statistics and Data Science National University of Singapore 6 Science Drive 2, Singapore 117546

  13. PR-Smoother: Simulator-Preserving Non-Gaussian Smoothing for Data Assimilation

    Sep 22, 2026Yuta TarumiAmortized InferenceData Assimilation

    iTHEMS RIKEN Kobe, Japan

  14. Taking a Second Look: Correcting Sea Ice Forecasts with Sparse Observations

    Sep 21, 2026Tianshuo Zhang, Xianglei Xing, Aowen Yang +4Data AssimilationSpatiotemporal Forecasting

    College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China

  15. FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation

    Sep 19, 2026Shiwei Ni, Yangwen Zhang, Hang Qi +2Data AssimilationBayesian Filtering

    School of Mathematical Sciences, Tongji University, Shanghai, China · Department of Mathematics, University of South Carolina, Columbia, SC 29208, USA

  16. Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning

    Sep 14, 2026Ashwin Suriyanarayanan, Romit MaulikData AssimilationReduced-Order Modeling

    School of Mechanical Engineering, Purdue University, West Lafayette, IN-47906

  17. ADDA: a Modular Framework for Representing, Simulating and Assimilating Dynamics with End-to-end Differentiability

    Aug 24, 2026Anthony Frion, Vien Minh Nguyen-Thanh, Ali Can Bekar +3Data AssimilationDifferentiable Programming

    Model-Driven Machine Learning Institute of Coastal Systems - Analysis and Modeling Helmholtz-Zentrum Hereon Germany

  18. Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems

    Aug 13, 2026Mrigank Dhingra, Ramchandran Muthukumar, Rebecca Willett +1Data AssimilationIterative Refinement

    Department of Mechanical and Aerospace Engineering, University of Tennessee, Knoxville, TN, 37996, USA · UChicago Data Science Institute, University of Chicago, Chicago, IL, 60637, USA

  19. Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching

    Aug 5, 2026Dibyajyoti Chakraborty, Romit MaulikData AssimilationSpatiotemporal Forecasting

    Information Sciences and Technology The Pennsylvania State University University Park, Pennsylvania · School of Mechanical Engineering Purdue University West Lafayette, Indiana

  20. Cardiovascular Digital Twins from Physics Based to Data Driven Approaches

    Aug 3, 2026Emmanuel Lwele, Francis ChikwetoData AssimilationPhysics-Informed ML

    Materials and Engineering Research Institute, Sheffield Hallam University, Sheffield, United Kingdom. · Medical Engineering and Cardiology Department, Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai, Japan.

  21. Incomplete Observations Boost Evolutionary Performance in Ocean Modeling

    Jul 21, 2026Yangyang Kong, Yutong Jiang, Yanhai Gan +3Learning with Missing DataData Assimilation

    State Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China · School of Computer Science and Technology, Ocean University of China, Qingdao, China

  22. Ensemble Controlled-Flow Filtering for Implicit Data Assimilation

    Jul 14, 2026Zhuoyuan Li, Yue Zhao, Ming LiData Assimilation

    Institute for Functional Intelligent Materials, National University of Singapore, Singapore 117544, Singapore · School of Mathematical Sciences, Fudan University, Shanghai 200433, China

  23. Generative Model Proposal based Particle Filtering for Data Assimilation

    Jul 1, 2026Chandni Nagda, Mayank Shrivastava, Gudrun Thorkelsdottir +3Particle FilteringData Assimilation

    University of Illinois at Urbana-Champaign · 2NVIDIA

  24. Uncertainty quantification via conformal prediction in data assimilation

    Jun 25, 2026Catherine George, Alireza Javanmardi, Tijana Janjić +1Uncertainty QuantificationData Assimilation

    Mathematisches Institut für Maschinelles Lernen und Data Science, Katholische Universität Eichstätt-Ingolstadt · Institute of Informatics, LMU Munich, Munich Center for Machine Learning (MCML)

  25. Learning Probabilistic Filters with Strictly Proper Scoring Rules

    Jun 25, 2026Eviatar Bach, Ricardo Baptista, Jochen Bröcker +2Bayesian FilteringData Assimilation

    Department of Meteorology, University of Reading Brian Hoskins Building, Reading, RG6 6ET, UK · Department of Mathematics and Statistics, University of Reading Pepper Lane, Reading, RG6 6AX, UK · National Centre for Earth Observation Brian Hoskins Building, University of Reading, Reading, RG6 6ET, UK +2

  26. ΩΩ: Operator-based Mixture Ensemble for Generative Assimilation

    Jun 18, 2026Pouria Behnoudfar, Nan ChenData AssimilationPosterior Sampling

    University of Wisconsin-Madison, Department of Mathematics, Madison, WI, USA

  27. Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks

    Jun 10, 2026Hemanth Chandravamsi, Hangchuan Hu, Ponkrshnan Thiagarajan +1Kalman FilteringData Assimilation

    Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA

  28. Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques

    Jun 9, 2026Guido Di Federico, Wenchao Teng, Louis J. DurlofskyMarkov Chain Monte CarloLatent Diffusion Models

    Department of Energy Science & Engineering, Stanford University, Stanford, CA, 94305, USA

  29. Diffusion Model-Based Data Assimilation for Real-World Energy Consumption Forecasting

    May 27, 2026Ruoyu Hu, Dahai Yu, Feng Bao +2Time Series ForecastingData Assimilation

    Department of Mathematics, Florida State University, Tallahassee, 32306, Florida, USA · Department of Computer Science, Florida State University, Tallahassee, 32306, Florida, USA · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, 37831, Tennessee, USA

  30. Training-Free Bayesian Filtering with Generative Emulators

    May 19, 2026Thomas Savary, François Rozet, Gilles LouppeParticle FilteringBayesian Filtering

    SAIL, Montefiore institute, University of Liège, Belgium.