Data-driven discovery of Koopman eigenfunctions for control

4 Jul 2017Eurika KaiserJ. Nathan KutzSteven L. Brunton

Data-driven transformations that reformulate nonlinear systems in a linear framework have the potential to enable the prediction, estimation, and control of strongly nonlinear dynamics using linear systems theory. The Koopman operator has emerged as a principled linear embedding of nonlinear dynamics, and its eigenfunctions establish intrinsic coordinates along which the dynamics behave linearly... (read more)

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