Online learning of both state and dynamics using ensemble Kalman filters

DOI10.3934/fods.2020015zbMath1478.93687arXiv2006.03859OpenAlexW3034056916MaRDI QIDQ2072638

Alban Farchi, Quentin Malartic, Marc Bocquet

Publication date: 26 January 2022

Published in: Foundations of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.03859

zbMATH Keywords

parameter estimationchaotic dynamical systemsmachine learningdata assimilationensemble Kalman filteriterative ensemble Kalman filterlocal ensemble Kalman filter

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15) Filtering in stochastic control theory (93E11) Learning and adaptive systems in artificial intelligence (68T05) Computational methods for problems pertaining to geophysics (86-08)

Related Items (1)

Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components

Uses Software

• EnKF
• PDE-Net

Cites Work

• Unnamed Item
• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• Relation between two common localisation methods for the EnKF
• Identification of nonlinear systems using polynomial nonlinear state space models
• Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
• An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error
• Machine Learning: Deepest Learning as Statistical Data Assimilation Problems
• Ensemble Kalman inversion: a derivative-free technique for machine learning tasks
• Degenerate Kalman Filter Error Covariances and Their Convergence onto the Unstable Subspace
• The ensemble Kalman filter for combined state and parameter estimation

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