Flow Matching for Efficient and Scalable Data Assimilation

• URL: http://arxiv.org/abs/2508.13313v3
• Date: Sat, 27 Sep 2025 15:59:08 GMT
• Title: Flow Matching for Efficient and Scalable Data Assimilation
• Authors: Taos Transue, Bohan Chen, So Takao, Bao Wang,
• Abstract summary: Data assimilation estimates a dynamical system's state from noisy observations.<n>Recent generative models like the ensemble score filter (EnSF) improve DA in high-dimensional nonlinear settings but are computationally expensive.<n>We introduce the ensemble flow filter (EnFF), a training-free, flow matching (FM)-based framework that accelerates sampling and offers flexibility in flow design.
• Score: 11.620677508344981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data assimilation (DA) estimates a dynamical system's state from noisy observations. Recent generative models like the ensemble score filter (EnSF) improve DA in high-dimensional nonlinear settings but are computationally expensive. We introduce the ensemble flow filter (EnFF), a training-free, flow matching (FM)-based framework that accelerates sampling and offers flexibility in flow design. EnFF uses Monte Carlo estimators for the marginal flow field, localized guidance for observation assimilation, and utilizes a novel flow that exploits the Bayesian DA formulation. It generalizes classical filters such as the bootstrap particle filter and ensemble Kalman filter. Experiments on high-dimensional benchmarks demonstrate EnFF's improved cost-accuracy tradeoffs and scalability, highlighting FM's potential for efficient, scalable DA. Code is available at https://github.com/Utah-Math-Data-Science/Data-Assimilation-Flow-Matching.

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