Year: 2025
Type: preprint
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 path 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.
Source: arXiv (Cornell University)
Authors: Transue, Taos, Chen, Bohan, Takao, So, Wang, Bao
Institution: California Institute of Technology
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: green
Awards: Collaborative Research: Algorithms, Theory, and Validation of Deep Graph Learning with Limited Supervision: A Continuous Perspective, Collaborative Research: MATH-DT: Mathematical Foundations of AI-assisted Digital Twins for High Power Laser Science and Engineering, G8112523353, RELIABLE, SCALABLE, AND DATA-EFFICIENT RANDOMIZED GRAPH NEURAL NETWORKS FOR NEURAL COMBINATORIAL OPTIMIZATION WITH SCIENTIFIC APPLICATIONS, Collaborative Research: ATD: Fast Algorithms and Novel Continuous-depth Graph Neural Networks for Threat Detection
OpenAlex ID: W4415330503
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