arXiv Machine Learning
Jun 19
Flow Matching for Efficient and Scalable Data Assimilation
arXiv:2508. 13313v4 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates a dynamical system's state from noisy observations.
EnJoi: Ensemble Joint Score Filter for Generative Data Assimilation
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arXiv Machine Learning
Sep 24
Improving Ensemble Filters with Flow Matching
arXiv:2609.28015v1 Announce Type: cross Abstract: Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis upd...
arXiv Machine Learning
Jul 15
Ensemble Controlled-Flow Filtering for Implicit Data Assimilation
arXiv:2607. 12975v1 Announce Type: cross Abstract: Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations.
Hugging Face Trending Papers
Jul 14
Ensemble Controlled-Flow Filtering for Implicit Data Assimilation
Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations. Many observation mechanisms, however, are many-to-one, implicit, non-smooth, or accessible only through simulation, and need not provide the residual structures or likelihood guidance required by existing ensemble filters.
arXiv Machine Learning
Sep 21
Amortized Filtering and Smoothing with Conditional Normalizing Flows
arXiv:2604.07169v3 Announce Type: replace-cross Abstract: Bayesian filtering and smoothing are central to data assimilation in nonlinear dynamical systems. Recent advances in deep generative models p...
arXiv Machine Learning
Aug 6
Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Flow-matching
arXiv:2608. 05103v1 Announce Type: new Abstract: Data assimilation (DA) uses Bayesian inference to update the state of a numerical forecast model with observed data.
arXiv Machine Learning
Aug 7
Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching
arXiv:2608. 05103v2 Announce Type: replace Abstract: Data assimilation (DA) uses Bayesian inference to update the state of a numerical forecast model with observed data.
arXiv Machine Learning
Jul 2
Generative Model Proposal based Particle Filtering for Data Assimilation
arXiv:2607. 01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications.
arXiv Machine Learning
Sep 23
FREESIA: Covariance-Aware Posterior Transport for Expressive and Scalable Data Assimilation
The paper introduces FREESIA, a training‑free, covariance‑aware posterior transport method for data assimilation that embeds forecast cross‑covariance into a flow‑based transport to recover unobserved states while preserving non‑Gaussian posterior structure. It combines an observation‑adaptive proposal with posterior correction, providing an asymptotically exact approximation of nonlinear posteriors and a Wasserstein error bound. Experiments on Double‑Well, Lorenz‑96, and Kolmogorov flow demonstrate that FREESIA captures complex posterior structures and achieves up to a 56% reduction in RMSE compared to the best baseline in sparse, nonlinear, non‑injective observation scenarios.
arXiv Machine Learning
Jun 26
Learning Probabilistic Filters with Strictly Proper Scoring Rules
arXiv:2606. 26497v1 Announce Type: new Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system, given observations, in an online fashion.
arXiv Machine Learning
Jun 9
ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
arXiv:2605. 14285v2 Announce Type: replace-cross Abstract: Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and climate science.
arXiv Machine Learning
Jun 2
Ensemble Score Filtering for Real-Data Energy Consumption Forecast Correction
arXiv:2605. 29072v2 Announce Type: replace Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management.