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Dynamically learning the parameters of a chaotic system using partial observations
[article]
2021
arXiv
pre-print
Motivated by recent progress in data assimilation, we develop an algorithm to dynamically learn the parameters of a chaotic system from partial observations. Under reasonable assumptions, we rigorously establish the convergence of this algorithm to the correct parameters when the system in question is the classic three-dimensional Lorenz system. Computationally, we demonstrate the efficacy of this algorithm on the Lorenz system by recovering any proper subset of the three non-dimensional
arXiv:2108.08354v1
fatcat:scvovu6cancftk2ahbsugu3hai