## Abstract

The Koopman operator is a *linear* but infinite-dimensional operator that governs the evolution of scalar observables defined on the state space of an autonomous dynamical system and is a powerful tool for the analysis and decomposition of nonlinear dynamical systems. In this manuscript, we present a data-driven method for approximating the leading *eigenvalues, eigenfunctions, and modes* of the Koopman operator. The method requires a data set of snapshot pairs and a dictionary of scalar observables, but does not require explicit governing equations or interaction with a “black box” integrator. We will show that this approach is, in effect, an extension of dynamic mode decomposition (DMD), which has been used to approximate the Koopman eigenvalues and modes. Furthermore, if the data provided to the method are generated by a Markov process instead of a deterministic dynamical system, the algorithm approximates the eigenfunctions of the Kolmogorov backward equation, which could be considered as the “stochastic Koopman operator” (Mezic in Nonlinear Dynamics 41(1–3): 309–325, 2005). Finally, four illustrative examples are presented: two that highlight the quantitative performance of the method when presented with either deterministic or stochastic data and two that show potential applications of the Koopman eigenfunctions.

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## Acknowledgments

The authors would like to thank Igor Mezić, Jonathan Tu, Maziar Hemati, and Scott Dawson for interesting and useful discussions on dynamic mode decomposition and the Koopman operator. M.O.W. gratefully acknowledges support from NSF DMS-1204783. I.G.K acknowledges support from AFOSR FA95550-12-1-0332 and NSF CMMI-1310173. C.W.R acknowledges support from AFOSR FA9550-12-1-0075.

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Communicated by Oliver Junge.

## Appendix: EDMD with Redundant Dictionaries

### Appendix: EDMD with Redundant Dictionaries

In this appendix, we present a simple example of applying EDMD to a problem where the elements of \({\mathcal {D}}\) contain redundancies (i.e., the elements of \({\mathcal {D}}\) are not a basis for \({\mathcal {F}}_{{\mathcal {D}}} \subset {\mathcal {F}}\)). Given full knowledge of the underlying dynamical system, one would always choose the elements of \({\mathcal {D}}\) to be a basis for \({\mathcal {F}}_{{\mathcal {D}}}\), but due to our ignorance of \({\mathcal {M}}\), a redundant set of functions may be chosen. Our objective here is to demonstrate that accurate results can still be obtained even if such a choice is made. To separate quadrature errors from errors resulting from our choice of \({\mathcal {D}}\), we assume that *M* is large enough that the EDMD method has already converged to a Galerkin method in that the residual is orthogonal to the space spanned by \({\mathcal {D}}\).

For the purposes of demonstration, we replace \(\mathcal {K}\) with \(\mathcal {L}=\partial _s^2\), the Laplace–Beltrami operator defined on the manifold \({\mathcal {M}}\), where \((x,y) = (s, s)\) for \(s\in [0, 2\pi )\) with periodic boundary conditions, which would correspond to, say, the EDMD procedure applied to a diffusion process on a periodic domain. A useful basis for this problem would be \(\tilde{\psi }_k(x,y) = \exp (\imath ks) = \exp (\imath k(x+y))\), but without prior knowledge of \({\mathcal {M}}\), it is difficult to determine this choice should be made. Because the problem appears two-dimensional, one may choose a dictionary whose elements have the form \(\psi _{m, n}(x, y) = \exp (\imath m x + \imath ny)\), which contains the \(\tilde{\psi }\) but is not linearly independent on \({\mathcal {M}}\). The indexes we use for the set of functions are \(\psi _k(x,y) = \psi _{m, n}(x,y)\) with \(m = (k \mod K) - K/2\) and \(n = \left\lfloor \frac{k}{K}\right\rfloor - K/2\) with \(k = 0, 1, \ldots , K^2\). Here, \(K\in \mathbb {N}\) is the total number of basis functions in a single spatial dimension.

Following (12), the *i*, *j*th element of \(\varvec{G}\) is

Similarly,

The diagonal structure we would normally have has been replaced with a more complex sparsity pattern, and it has a large nullspace (when \(K=8\), the nullspace is 50-dimensional). To reiterate, there are no advantages to this choice; the redundancies in \({\mathcal {D}}\) appear due to ignorance about the nature of \({\mathcal {M}}\), which is the expected situation. Because \(\varvec{G}\) is singular, the use of the pseudoinverse in (12) is *critical* to obtain a unique solution.

However, once this is done, there is excellent agreement between the leading eigenfunctions and eigenvalues of \(\mathcal {L}\) and those computed using EDMD; this is shown in Fig. 14. The nonzero eigenvalues are quantitatively correct; in particular, pairs of eigenvalues of the form \(\lambda =-k^2\) are obtained up until \(k=8\) using \(K = 8\). Although the maximum (absolute) value of *m* or *n* is only 4, it is clear that the superposition of these functions on \({\mathcal {M}}\) mimics \(k = 8\) modes. The associated eigenfunctions are shown in Fig. 14c; again, there is excellent agreement between the analytic solution (i.e., \(\exp (-\imath ks)\)) and the EDMD computed solution.

The resulting eigenfunctions can also be evaluated for \((x,y)\not \in {\mathcal {M}}\), but the functions have no dynamical meaning there. Indeed, their value is determined entirely by the regularization used and has no relationship to the underlying dynamical system, which is defined solely on \({\mathcal {M}}\). This should be contrasted to related works such as Froyland et al. (2014) where the dynamical system is truly defined on \(\Omega \), and \({\mathcal {M}}\) is simply the slow manifold where the eigenfunctions evaluated at \((x,y)\not \in {\mathcal {M}}\) are meaningful as they contain information about the fast dynamics of the system.

Overall, the performance of the EDMD procedure is dependent upon the subspace, \({\mathcal {F}}_{{\mathcal {D}}}\) and not the precise choice of \({\mathcal {D}}\). There are numerical advantages of choosing \({\mathcal {D}}\) to be a basis for \({\mathcal {F}}_{{\mathcal {D}}}\), but in many circumstances this cannot be done without prior knowledge of \({\mathcal {M}}\). As a result, there are likely benefits to combining EDMD with manifold learning techniques [see, e.g., Coifman and Lafon (2006), Lee and Verleysen (2007), Erban et al. (2007) and Sirisup et al. (2005)]. These methods can numerically approximate \({\mathcal {M}}\), which could allow a more effective choice of the elements of \({\mathcal {D}}\) and their associated numerical benefits. As shown here, these methods are not essential to the algorithm, but \({\mathcal {M}}\) must be identified through some means if EDMD is to be used for more than just data analysis.

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Williams, M.O., Kevrekidis, I.G. & Rowley, C.W. A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition.
*J Nonlinear Sci* **25**, 1307–1346 (2015). https://doi.org/10.1007/s00332-015-9258-5

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DOI: https://doi.org/10.1007/s00332-015-9258-5