发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高维非线性动力学与稀疏观测下的数据同化难题，提出LD-EnFF框架，在潜空间进行预测与滤波更新，结合潜动力学代理和VAE观测模型，显著优于多种基准算法。
AI 中文摘要
数据同化将模型预测与含噪声的不完整观测相结合，以估计动力系统的演化状态。现有方法面临两个叠加的挑战：高维非线性动力学使得重复的前向模拟计算成本高昂，而稀疏观测仅提供关于完整状态的有限直接信息。为应对这些挑战，我们提出了潜动力学集成流滤波器（LD-EnFF），这是一种在紧凑潜空间中进行预测传播和滤波更新的序贯贝叶斯滤波框架。LD-EnFF结合了用于集成传播的潜动力学代理模型和基于变分自编码器（VAE）的观测模型，该模型在潜空间中评估依赖于状态的观测似然。在每个同化步骤中，基于流匹配的集成滤波更新利用预测集成和该似然生成后验样本，同时更新潜状态和不确定参数。这种设计避免了预测过程中的重复全状态模拟和似然评估中的全场重建。LD-EnFF在涵盖Kolmogorov流、海啸传播和大气建模的基准测试中，显著优于广泛的数据同化算法，所有这些基准均具有复杂动力学和稀疏、含噪观测。
英文摘要
Data assimilation combines model forecasts with noisy, incomplete observations to estimate the evolving state of a dynamical system. Existing methods face two compounding challenges: high-dimensional nonlinear dynamics make repeated forward simulation computationally expensive, while sparse observations provide limited direct information about the full state. To address these challenges, we propose the Latent-Dynamics Ensemble Flow Filter (LD-EnFF), a sequential Bayesian filtering framework that performs both forecast propagation and filtering updates in a compact latent space. LD-EnFF combines a latent dynamics surrogate for ensemble propagation with a variational autoencoder (VAE)-based observation model that evaluates a state-dependent observation likelihood in latent space. At each assimilation step, an ensemble filtering update based on flow matching uses the forecast ensemble and this likelihood to generate posterior samples, jointly updating latent states and uncertain parameters. This design avoids repeated full-state simulation during forecasting and full-field reconstruction during likelihood evaluation. LD-EnFF substantially outperforms a broad range of data assimilation algorithms on benchmarks spanning Kolmogorov flow, tsunami propagation, and atmospheric modeling, all featuring complex dynamics and sparse, noisy observations.
CommentsSubmitted to ICLR 2027
