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koopman operater kernel
We consider the application of Koopman theory to nonlinear partial differential equations. We
demonstrate that the observables chosen for constructing the Koopman operator are critical for en-
abling an accurate approximation to the nonlinear dynamics. If such observables can be found, then
the dynamic mode decomposition (DMD) algorithm can be enacted to compute a finite-dimensional
approximation of the Koopman operator, including its eigenfunctions, eigenvalues and Koopman
modes. We demonstrate simple rules of thumb for selecting a parsimonious set of observables that
can greatly improve the approximation of the Koopman operator. Further, we show that the clear
goal in selecting observables is to place the DMD eigenvalues on the imaginary axis, thus giving
an objective function for observable selection. Judiciously chosen observables lead to physically
interpretable spatio-temporal features of the complex system under consideration and provide a
connection to manifold learning methods. Our method provides a valuable intermediate, yet inter-
pretable, approximation to the Koopman operator that lies between the DMD method and the com-
putationally intensive extended DMD (EDMD). We demonstrate the impact of observable selection,
including kernel methods, and construction of the Koopman operator on several canonical, nonlinear
PDEs: Burgers’ equation, the nonlinear Schrödinger equation, the cubic-quintic Ginzburg-Landau
equation and a reaction-diffusion system. These examples serve to highlight the most pressing and
critical challenge of Koopman theory: a principled way to select appropriate observables
2018-10-20
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