用生成模型实现无需训练的高效贝叶斯滤波，突破高维系统瓶颈。
Training-Free Bayesian Filtering with Generative Emulators
- 利用扩散模型构建动力系统代理，直接生成状态分布
- 在大气动力学等高维混沌系统上实现可扩展的粒子滤波
- 无需额外训练，解决传统方法难以处理高维问题的痛点
贝叶斯滤波旨在从观测中估计动态系统的合理状态。现有方法中，粒子滤波理论上适用于非线性动态和观测，但在高维场景下可扩展性差。本文表明，基于扩散的动态系统生成代理可无需额外训练，实现一种此前因经典数值求解器难题而未被充分探索的最优粒子滤波变体。在非线性混沌系统（包括大气动力学）上的实验表明，该方法成功将粒子滤波拓展至高维设置。
原文摘要 · Abstract (English)
Bayesian filtering is a well-known problem that aims to estimate plausible states of a dynamical system from observations. Among existing approaches to solve this problem, particle filters are theoretically exact for non-linear dynamics and observations, but suffer from poor scalability in high dimensions. In this work, we show that diffusion-based emulators of dynamical systems can be used to implement, without additional training, an optimal variant of particle filters that has remained largely unexplored due to implementation challenges with classical numerical solvers. Experiments on nonlinear chaotic systems, including atmospheric dynamics, demonstrate that the proposed approach successfully scales particle filtering to high-dimensional settings.
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