Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations

DOI10.1063/5.0066080zbMath1548.37145MaRDI QIDQ6557699

Sebastian Reich, Georg A. Gottwald

Publication date: 18 June 2024

Published in: Chaos (Search for Journal in Brave)

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and numerical treatment of dynamical systems (37M99)

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Related Items (6)

Asymptotic behavior of the forecast-assimilation process with unstable dynamics ⋮ A Koopman-Takens theorem: linear least squares prediction of nonlinear time series ⋮ Data driven adaptive Gaussian mixture model for solving Fokker-Planck equation ⋮ Data-driven stochastic model for cross-interacting processes with different time scales ⋮ Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: a chaotic Kuramoto-Sivashinsky test case ⋮ CGNSDE: conditional Gaussian neural stochastic differential equation for modeling complex systems and data assimilation

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