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.
The Ensemble Schr{\"o}dinger Bridge filter for Nonlinear Data Assimilation
arXiv:2512. 18928v4 Announce Type: replace Abstract: This work introduces a novel nonlinear optimal filtering method, termed the Ensemble Schr{\"o}dinger Bridge nonlinear filter.
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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
Jun 11
Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks
arXiv:2602. 23461v2 Announce Type: replace-cross Abstract: Data assimilation (DA) for compressible flows with shocks is challenging because many classical DA methods generate spurious oscillations and nonphysical features near uncertain shocks.
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.
arXiv Machine Learning
Jun 29
Diffusion Model-Based Data Assimilation for Real-World Energy Consumption Forecasting
arXiv:2605. 29072v3 Announce Type: replace Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management.
arXiv Machine Learning
Jun 26
Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
arXiv:2508. 18486v2 Announce Type: replace-cross Abstract: Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP).
arXiv Machine Learning
Jun 30
Decomposing Ensemble Spread in Lorenz '96 With Learned Stochastic Parameterizations
arXiv:2605. 22242v3 Announce Type: replace Abstract: Weather and climate forecasts are inherently uncertain due to chaotic dynamics, imperfect initial conditions, and incomplete representation of the underlying physical processes.
arXiv Machine Learning
Jul 17
A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
arXiv:2511. 06609v4 Announce Type: replace Abstract: The accurate forecasting of complex, high-dimensional dynamical systems from observational data is a fundamental task across numerous scientific and engineering disciplines.
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
Conditional Normalizing Flows for Forward and Backward Joint State and Parameter Estimation
arXiv:2601. 07013v2 Announce Type: replace-cross Abstract: Traditional filtering algorithms for state estimation -- such as classical Kalman filtering, unscented Kalman filtering, and particle filters -- show performance degradation when applied to nonlinear systems whose uncertainty follows arbitrary non-Gaussian, and potentially multi-modal distributions.
arXiv Machine Learning
Jun 19
Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
arXiv:2604. 21097v2 Announce Type: replace-cross Abstract: Chaos arises in many complex dynamical systems, from weather to power grids, but is difficult to accurately model with data-driven methods such as machine learning emulators.