Generative Model Proposal based Particle Filtering for Data Assimilation

• URL: http://arxiv.org/abs/2607.01012v2
• Date: Fri, 03 Jul 2026 18:08:14 GMT
• Title: Generative Model Proposal based Particle Filtering for Data Assimilation
• Abstract summary: Flow Proposal Particle Filters learn a conditional generative model based proposal approximating the variance-minimizing optimal proposal for particle propagation.<n>Since our proposal admits tractable likelihood evaluation, FPPF computes accurate importance weights and retains a Bayesian update step.
• Score: 13.045651122066927
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications. In the filtering setting, the goal is to model the posterior over the current state given all observations so far. Classical solutions typically make simplifying distributional or functional assumptions, e.g., linear-Gaussian systems, which can be inaccurate in many scenarios. In principle, particle filters (PFs) remove these assumptions, yet often collapse in high dimensions. Recent generative approaches learn conditional state transitions, but without principled Bayesian updates they do not recover the correct filtering posterior and can accumulate error over long horizons. In this work, we introduce Flow Proposal Particle Filters (FPPF), which learn a conditional generative model based proposal approximating the variance-minimizing optimal proposal for particle propagation. Conditioning on observations steers particles toward high-likelihood regions before weighting, reducing weight variance and delaying degeneracy. Since our proposal admits tractable likelihood evaluation, FPPF computes accurate importance weights and retains a Bayesian update step. We further extend FPPF to high-dimensional problems through localization strategies, adressing another standard PF failure mode. Extensive experiments on a variety of dynamical systems show that FPPF outperforms statistical baselines and other generative methods in non-linear, non-Gaussian, and high-dimensional regimes.

Related papers

• Flow Matching for Efficient and Scalable Data Assimilation [11.620677508344981]
  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.
  arXiv  Detail & Related papers  (2025-08-18T19:00:45Z)
• Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling [70.8832906871441]
  We study how to steer generation toward desired rewards without retraining the models.<n>Prior methods typically resample or filter within a single denoising trajectory, optimizing rewards step-by-step without trajectory-level refinement.<n>We introduce particle Gibbs sampling for diffusion language models (PG-DLM), a novel inference-time algorithm enabling trajectory-level refinement while preserving generation perplexity.
  arXiv  Detail & Related papers  (2025-07-11T08:00:47Z)
• Flow-based Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems [4.382988524355736]
  We propose a flow-based Bayesian filter (FBF) that integrates normalizing flows to construct a novel latent linear state-space model with Gaussian filtering distributions.<n>This framework facilitates efficient density estimation and sampling using invertible transformations provided by normalizing flows.<n> Numerical experiments demonstrate the superior accuracy and efficiency of FBF.
  arXiv  Detail & Related papers  (2025-02-22T14:04:23Z)
• Adversarial Transform Particle Filters [11.330617592263744]
  The particle filter (PF) and the ensemble Kalman filter (EnKF) are widely used for approximate inference in state-space models.<n>We propose the Adversarial Transform Particle Filter (ATPF), a novel filtering framework that combines the strengths of the PF and the EnKF through adversarial learning.
  arXiv  Detail & Related papers  (2025-02-10T05:31:35Z)
• Closed-form Filtering for Non-linear Systems [83.91296397912218]
  We propose a new class of filters based on Gaussian PSD Models, which offer several advantages in terms of density approximation and computational efficiency. We show that filtering can be efficiently performed in closed form when transitions and observations are Gaussian PSD Models. Our proposed estimator enjoys strong theoretical guarantees, with estimation error that depends on the quality of the approximation and is adaptive to the regularity of the transition probabilities.
  arXiv  Detail & Related papers  (2024-02-15T08:51:49Z)
• Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms [88.74308282658133]
  Reization (RP) Policy Gradient Methods (PGMs) have been widely adopted for continuous control tasks in robotics and computer graphics. Recent studies have revealed that, when applied to long-term reinforcement learning problems, model-based RP PGMs may experience chaotic and non-smooth optimization landscapes. We propose a spectral normalization method to mitigate the exploding variance issue caused by long model unrolls.
  arXiv  Detail & Related papers  (2023-10-30T18:43:21Z)
• Nonlinear Filtering with Brenier Optimal Transport Maps [6.048942663334447]
  This paper is concerned with the problem of nonlinear filtering, i.e., computing the conditional distribution of the state of a dynamical system.<n>Conventional sequential importance resampling (SIR) particle filters suffer from fundamental limitations, in scenarios involving degenerate likelihoods or high-dimensional states.<n>In this paper, we explore an alternative method, which is based on estimating the Brenier optimal transport (OT) map from the current prior distribution of the state to the posterior distribution at the next time step.
  arXiv  Detail & Related papers  (2023-10-21T01:34:30Z)
• Exact nonlinear state estimation [0.0]
  The majority of data assimilation methods in the geosciences are based on Gaussian assumptions.<n>Non-parametric, particle-based DA algorithms have superior accuracy, but their application to high-dimensional models still poses operational challenges.<n>This article introduces a new nonlinear estimation theory which attempts to bridge the existing gap in DA methodology.
  arXiv  Detail & Related papers  (2023-10-17T03:44:29Z)
• ManiFlow: Implicitly Representing Manifolds with Normalizing Flows [145.9820993054072]
  Normalizing Flows (NFs) are flexible explicit generative models that have been shown to accurately model complex real-world data distributions. We propose an optimization objective that recovers the most likely point on the manifold given a sample from the perturbed distribution. Finally, we focus on 3D point clouds for which we utilize the explicit nature of NFs, i.e. surface normals extracted from the gradient of the log-likelihood and the log-likelihood itself.
  arXiv  Detail & Related papers  (2022-08-18T16:07:59Z)
• Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions [65.74069050283998]
  We propose a computational framework to approximate Doob's $h$-transforms. The proposed approach can be orders of magnitude more efficient than state-of-the-art particle filters.
  arXiv  Detail & Related papers  (2022-06-07T15:03:05Z)
• When is Particle Filtering Efficient for Planning in Partially Observed Linear Dynamical Systems? [60.703816720093016]
  This paper initiates a study on the efficiency of particle filtering for sequential planning. We are able to bound the number of particles needed so that the long-run reward of the policy based on particle filtering is close to that based on exact inference. We believe this technique can be useful in other sequential decision-making problems.
  arXiv  Detail & Related papers  (2020-06-10T17:43:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.