References
Andreu, Fuensanta, Ballester, Coloma, Caselles, Vicent, and Mazón, José. 2001. Minimizing total variation flow. Differential and Integral Equations, 14(3), 321–360.
Askham, Travis, and Kutz, J. Nathan. 2018. Variable projection methods for an optimized dynamic mode decomposition. SIAM Journal on Applied Dynamical Systems, 17(1), 380–416.
Aubry, Nadine, Guyonnet, Régis, and Lima, Ricardo. 1991. Spatiotemporal analysis of complex signals: Theory and applications. Journal of Statistical Physics, 64(3), 683–739.
Balakrishnan, Alampallam. 1966. On the controllability of a nonlinear system. Proceedings of the National Academy of Sciences of the United States of America, 55(3), 465–468.
Bellettini, Giovanni, Caselles, Vicent, and Novaga, Matteo. 2002. The total variation flow in RN. Journal of Differential Equations, 184(2), 475–525.
Bollt, Erik. 2021. Geometric considerations of a good dictionary for Koopman analysis of dynamical systems: Cardinality, “primary eigenfunction,” and efficient representation. Communications in Nonlinear Science and Numerical Simulation, 100, 105833.
Brezis, Haim. 1973. Opérateurs maximaux monotones et semi-groupes de contractions dans les espaces de Hilbert. Elsevier.
Brunton, Steven, Budišić, Marko, Kaiser, Eurika, and Kutz, J. Nathan. 2021. Modern Koopman theory for dynamical systems. SIAM Review, 64(2), 229–340. arXiv:2102.12086.
Brunton, Steven, and Kutz, J. Nathan. 2019. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press.
Brunton, Steven, Proctor, Joshua, and Kutz, J. Nathan. 2016. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15), 3932–3937.
Bungert, Leon, and Burger, Martin. 2019. Asymptotic profiles of nonlinear homogeneous evolution equations of gradient flow type. Journal of Evolution Equations, 20, 1061–1092.
Bungert, Leon, Burger, Martin, and Tenbrinck, Daniel. 2019a. Computing nonlinear eigenfunctions via gradient flow extinction. Pages 291–302 of International Conference on Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science book series, volume 11603. Springer. DOI: https://doi.org/10.1007/978-3-030-22368-7_23. Bungert, Leon, Burger, Martin, Chambolle, Antonin, and Novaga, Matteo. 2019b. Nonlinear spectral decompositions by gradient flows of one-homogeneous functionals. Analysis & PDE, 14(3), 823–860. arXiv:1901.06979.
Burger, Martin, Gilboa, Guy, Moeller, Michael, Eckardt, Lina, and Cremers, Daniel. 2016. Spectral decompositions using one-homogeneous functionals. SIAM Journal on Imaging Sciences, 9(3), 1374–1408.
Chuaqui, Martin. 2018. General criteria for curves to be simple. Journal of Mathematical Analysis and Applications, 464(1), 955–963.
Cohen, Ido, Azencot, Omri, Lifshits, Pavel, and Gilboa, Guy. 2021a. Modes of homogeneous gradient flows. SIAM Journal on Imaging Sciences, 14(3), 913–945.
Cohen, Ido, Berkov, Tom, and Gilboa, Guy. 2021b. Total-variation mode decomposition. Pages 52–64 of Scale Space and Variational Methods in Computer Vision, edited by Elmoataz, Abderrahim, Fadili, Jalal, Quéau, Yvain, Rabin, Julien, and Loїc Simon. Springer.
Cohen, Ido, Falik, Adi, and Gilboa, Guy. 2019. Stable explicit p-Laplacian flows based on nonlinear eigenvalue analysis. Pages 315–327 of International Conference on Scale Space and Variational Methods in Computer Vision, edited by Lellmann, Jan, Modersitzki, Jan, and Burger, Martin. Lecture Notes in Computer Science, volume 11603. Springer.
Courant, Richard, and John, Fritz. 2012. Introduction to Calculus and Analysis I. Springer.
Dawson, Scott, Hemati, Maziar, Williams, Matthew, and Rowley, Clarence. 2016. Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition. Experiments in Fluids, 57(3), 42. DOI: https://doi.org/10.1007/s00348-016-2127-7. Elad, Michael. 2010. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer.
Evangelisti, E. 2011. Controllability and Observability: Lectures Given at a Summer School of the Centro Internazionale Matematico Estivo (CIME) held in Pontecchio (Bologna), Italy, July 1 –9,1968. CIME Summer Schools, volume 46. Springer.
Gavish, Matan, and Donoho, David. 2014. The optimal hard threshold for singular values is
. IEEE Transactions on Information Theory, 60(8), 5040–5053.
Giannakis, Dimitrios. 2019. Data-driven spectral decomposition and forecasting of ergodic dynamical systems. Applied and Computational Harmonic Analysis, 47(2), 338–396.
Giannakis, Dimitrios, and Majda, Andrew. 2012. Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability. Proceedings of the National Academy of Sciences, 109(7), 2222–2227.
Gilboa, Guy. 2014. A total variation spectral framework for scale and texture analysis. SIAM Journal on Imaging Sciences, 7(4), 1937–1961.
Gilboa, Guy. 2018. Nonlinear Eigenproblems in Image Processing and Computer Vision. Springer.
Gilboa, Guy, and Osher, Stanley. 2009. Nonlocal operators with applications to image processing. Multiscale Modeling & Simulation, 7(3), 1005–1028.
Hemati, Maziar, Rowley, Clarence, Deem, Eric, and Cattafesta, Louis. 2017. De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets. Theoretical and Computational Fluid Dynamics, 31(4), 349–368.
Kaiser, Eurika, Kutz, J. Nathan, and Brunton, Steven. 2018. Discovering conservation laws from data for control. Pages 6415–6421 of 2018 IEEE Conference on Decision and Control (CDC). Institute of Electrical and Electronics Engineers.
Kaiser, Eurika, Kutz, J. Nathan, and Brunton, Steven. 2021. Data-driven discovery of Koopman eigenfunctions for control. Machine Learning: Science and Technology, 2(3), 035023. DOI: https://doi.org/10.1088/2632-2153/abf0f5. Katzir, Oren. 2017. On the scale-space of filters and their applications. M.Phil. thesis, Technion – Israel Institute of Technology, Haifa.
Kawahara, Yoshinobu. 2016. Dynamic mode decomposition with reproducing kernels for Koopman spectral analysis. Advances in Neural Information Processing Systems, 29, 911–919.
Koopman, Bernard. 1931. Hamiltonian systems and transformation in Hilbert space. Proceedings of the National Academy of Sciences of the United States of America, 17(5), 315–318.
Korda, Milan, and Mezić, Igor. 2018. Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control. Automatica, 93, 149–160.
Kou, Shauying, Elliott, David, and Tarn, Tzyh Jong. 1973. Observability of nonlinear systems. Information and Control, 22(1), 89–99.
Kutz, J. Nathan, Brunton, Steven, Brunton, Bingni, and Proctor, Joshua. 2016a. Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems. SIAM.
Kutz, J. Nathan, Proctor, Joshua, and Brunton, Steven. 2018. Applied Koopman theory for partial differential equations and data-driven modeling of spatio-temporal systems. Complexity, 2018, 6010634.
Kutz, J. Nathan, Proctor, Joshua, and Brunton, Steven. 2016b. Koopman theory for partial differential equations. arXiv:1607.07076.
Langley, Pat, Bradshaw, Gary, and Simon, Herbert. 1981. BACON. 5: The discovery of conservation laws. Pages 121–126 of International Joint Conference on Artificial Intelligence, vol. 1. Citeseer.
Li, Qianxiao, Dietrich, Felix, Bollt, Erik, and Kevrekidis, Ioannis. 2017. Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(10), 103111.
Lu, Hannah, and Tartakovsky, Daniel. 2020. Prediction accuracy of dynamic mode decomposition. SIAM Journal on Scientific Computing, 42(3), A1639–A1662.
Mairal, Julien, Bach, Francis, Ponce, Jean, et al. 2014b. Sparse modeling for image and vision processing. Foundations and Trends® in Computer Graphics and Vision, 8(2–3), 85–283.
Mauroy, Alexandre. 2021. Koopman operator theory for infinite-dimensional systems: Extended dynamic mode decomposition and identification of nonlinear PDEs. Mathematics, 9(19), 2495. arXiv:2103.12458.
Mauroy, Alexandre, Mezić, Igor, and Moehlis, Jeff. 2013. Isostables, isochrons, and Koopman spectrum for the action–angle representation of stable fixed point dynamics. Physica D: Nonlinear Phenomena, 261, 19–30.
Mauroy, Alexandre, Susuki, Y, and Mezić, I. 2020. The Koopman Operator in Systems and Control. Springer.
Nakao, Hiroya, and Mezić, Igor. 2020. Spectral analysis of the Koopman operator for partial differential equations. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(11), 113131.
Otto, Samuel, and Rowley, Clarence. 2021. Koopman operators for estimation and control of dynamical systems. Annual Review of Control, Robotics, and Autonomous Systems, 4, 59–87.
Pan, Shaowu, Arnold-Medabalimi, Nicholas, and Duraisamy, Karthik. 2021. Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces. Journal of Fluid Mechanics, 917, A18. doi:https://doi.org/10.1017/jfm.2021.271. Rudy, Samuel, Brunton, Steven, Proctor, Joshua, and Kutz, J. Nathan. 2017. Data-driven discovery of partial differential equations. Science Advances, 3(4), e1602614.
Schmid, Peter. 2010. Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics, 656, 5–28.
Schmidt, Michael, and Lipson, Hod. 2009. Distilling free-form natural laws from experimental data. Science, 324(5923), 81–85.
Tu, Jonathan, Rowley, Clarence, Luchtenburg, Dirk, Brunton, Steven, and Kutz, J. Nathan. 2013. On dynamic mode decomposition: Theory and applications. Journal of Computational Dynamics, 1(2), 391–421. arXiv:1312.0041.
Valmorbida, Giorgio, and Anderson, James. 2017. Region of attraction estimation using invariant sets and rational Lyapunov functions. Automatica, 75(January), 37–45.
Venturi, Daniele, and Dektor, Alec. 2021. Spectral methods for nonlinear functionals and functional differential equations. Research in the Mathematical Sciences, 8(2), 1–39.
Williams, Matthew, Hemati, Maziar, Dawson, Scott, Kevrekidis, Ioannis, and Rowley, Clarence. 2016. Extending data-driven Koopman analysis to actuated systems. IFAC-PapersOnLine, 49(18), 704–709.
Williams, Matthew, Kevrekidis, Ioannis, and Rowley, Clarence 2015a. A data-driven approximation of the Koopman operator: Extending dynamic mode decomposition. Journal of Nonlinear Science, 25(6), 1307–1346.
Williams, Matthew, Rowley, Clarence, Mezić, Igor, and Kevrekidis, Ioannis. 2015b. Data fusion via intrinsic dynamic variables: An application of data-driven Koopman spectral analysis. Europhysics Letters, 109(4), 40007.