FlowDAS: A Flow-Based Framework for Data Assimilation

• URL: http://arxiv.org/abs/2501.16642v1
• Date: Mon, 13 Jan 2025 05:03:41 GMT
• Title: FlowDAS: A Flow-Based Framework for Data Assimilation
• Authors: Siyi Chen, Yixuan Jia, Qing Qu, He Sun, Jeffrey A Fessler,
• Abstract summary: FlowDAS is a novel generative model-based framework using the interpolants to unify the learning of state transition dynamics and generative priors.<n>Our experiments demonstrate FlowDAS's superior performance on various benchmarks, from the Lorenz system to high-dimensional fluid superresolution tasks.
• Score: 15.64941169350615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data assimilation (DA) is crucial for improving the accuracy of state estimation in complex dynamical systems by integrating observational data with physical models. Traditional solutions rely on either pure model-driven approaches, such as Bayesian filters that struggle with nonlinearity, or data-driven methods using deep learning priors, which often lack generalizability and physical interpretability. Recently, score-based DA methods have been introduced, focusing on learning prior distributions but neglecting explicit state transition dynamics, leading to limited accuracy improvements. To tackle the challenge, we introduce FlowDAS, a novel generative model-based framework using the stochastic interpolants to unify the learning of state transition dynamics and generative priors. FlowDAS achieves stable and observation-consistent inference by initializing from proximal previous states, mitigating the instability seen in score-based methods. Our extensive experiments demonstrate FlowDAS's superior performance on various benchmarks, from the Lorenz system to high-dimensional fluid super-resolution tasks. FlowDAS also demonstrates improved tracking accuracy on practical Particle Image Velocimetry (PIV) task, showcasing its effectiveness in complex flow field reconstruction.

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