Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics
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Publication:2124589
DOI10.1016/j.jcp.2020.109635OpenAlexW3034522316MaRDI QIDQ2124589
Publication date: 11 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2020.109635
block decompositionsparse identificationexpectation-maximization approachnonlinear optimal smootherphysics constraintshort training data
Related Items (3)
An efficient data-driven multiscale stochastic reduced order modeling framework for complex systems ⋮ A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parameterization ⋮ Improving the prediction of complex nonlinear turbulent dynamical systems using nonlinear filter, smoother and backward sampling techniques
Uses Software
Cites Work
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