Dynamically learning the parameters of a chaotic system using partial observations

DOI10.3934/dcds.2022033zbMath1502.37092arXiv2108.08354OpenAlexW3195982168MaRDI QIDQ2155717

Jared P. Whitehead, Joshua Hudson, Adam Larios, Vincent R. Martinez, Elizabeth Carlson, Eunice Ng

Publication date: 15 July 2022

Published in: Discrete and Continuous Dynamical Systems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.08354

zbMATH Keywords

parameter estimationinverse problemsLorenz equationsdata assimilationnudgingAzouani-Olson-Titi algorithm

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and numerical treatment of dynamical systems (37M99)

Related Items (7)

Concurrent MultiParameter Learning Demonstrated on the Kuramoto--Sivashinsky Equation ⋮ Data assimilation with model error: analytical and computational study for sabra shell model ⋮ Data assimilation using time-delay nudging in the presence of Gaussian noise ⋮ Accelerating and enabling convergence of nonlinear solvers for Navier-Stokes equations by continuous data assimilation ⋮ Removing Splitting/Modeling Error in Projection/Penalty Methods for Navier-Stokes Simulations with Continuous Data Assimilation ⋮ Super-exponential convergence rate of a nonlinear continuous data assimilation algorithm: the 2D Navier-Stokes equation paradigm ⋮ Continuous data assimilation of a discretized barotropic vorticity model of geophysical flow

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