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Computing continuous and piecewise affine lyapunov functions for nonlinear systems
A kernel-based method for data-driven koopman spectral analysis
1. | United Technologies Research Center, 411 Silver Lane, East Hartford, CT 06118, United States |
2. | Dept. of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 |
3. | Department of Chemical and Biological Engineering & PACM, Princeton University, Princeton, NJ 08544, United States |
References:
[1] |
S. Bagheri, Koopman-mode decomposition of the cylinder wake, Journal of Fluid Mechanics, 726 (2013), 596-623.
doi: 10.1017/jfm.2013.249. |
[2] |
S. Bagheri, Effects of weak noise on oscillating flows: Linking quality factor, Floquet modes, and Koopman spectrum, Physics of Fluids, 26 (2014), 094104.
doi: 10.1063/1.4895898. |
[3] |
G. Baudat and F. Anouar, Kernel-based methods and function approximation, In Proceedings of the International Joint Conference on Neural Networks, IEEE, 2 (2001), 1244-1249.
doi: 10.1109/IJCNN.2001.939539. |
[4] |
C. M. Bishop et al, Pattern Recognition and Machine Learning, Springer, 2006.
doi: 10.1007/978-0-387-45528-0. |
[5] |
J. P. Boyd, Chebyshev and Fourier Spectral Methods, Courier Dover Publications, Mineola, NY, 2001. |
[6] |
M. Budišić, R. Mohr and I. Mezić, Applied Koopmanism, Chaos: An Interdisciplinary Journal of Nonlinear Science, 22 (2012), 047510, 33pp.
doi: 10.1063/1.4772195. |
[7] |
C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2 (1998), 121-167. |
[8] |
A. Chatterjee, An introduction to the proper orthogonal decomposition, Current Science, 78 (2000), 808-817. |
[9] |
K. K. Chen, J. H. Tu and C. W. Rowley, Variants of dynamic mode decomposition: Boundary condition, Koopman, and Fourier analyses, Journal of Nonlinear Science, 22 (2012), 887-915.
doi: 10.1007/s00332-012-9130-9. |
[10] |
R. R. Coifman and S. Lafon, Diffusion maps, Applied and Computational Harmonic Analysis, 21 (2006), 5-30.
doi: 10.1016/j.acha.2006.04.006. |
[11] |
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
doi: 10.1017/CBO9780511801389. |
[12] |
C. E. Elmer and E. S. Van Vleck, Spatially discrete FitzHugh-Nagumo equations, SIAM Journal on Applied Mathematics, 65 (2005), 1153-1174.
doi: 10.1137/S003613990343687X. |
[13] |
P. Gaspard and S. Tasaki, Liouvillian dynamics of the {Hopf} bifurcation, Physical Review E, 64 (2001), 056232, 17pp.
doi: 10.1103/PhysRevE.64.056232. |
[14] |
M. S. Hemati, M. O. Williams and C. W. Rowley, Dynamic mode decomposition for large and streaming datasets, Physics of Fluids, 26 (2014), 111701.
doi: 10.1063/1.4901016. |
[15] |
P. Holmes, J. L. Lumley, G. Berkooz and C. W. Rowley, Turbulence, Coherent Structures, Dynamical Systems and Symmetry, Cambridge University Press, 2nd edition, 2012.
doi: 10.1017/CBO9780511919701. |
[16] |
M. R. Jovanović, P. J. Schmid and J. W. Nichols, Sparsity-promoting dynamic mode decomposition, Physics of Fluids, 26 (2014), 024103. |
[17] |
J.-N. Juang, Applied System Identification, Prentice Hall, 1994. |
[18] |
B. O. Koopman and J. von Neumann, Dynamical systems of continuous spectra, Proceedings of the National Academy of Sciences of the United States of America, 18 (1932), 255-163.
doi: 10.1073/pnas.18.3.255. |
[19] |
B. O. Koopman, Hamiltonian systems and transformation in Hilbert space, Proceedings of the National Academy of Sciences of the United States of America, 17 (1931), 315-318.
doi: 10.1073/pnas.17.5.315. |
[20] |
J. A. Lee and M. Verleysen, Nonlinear Dimensionality Reduction, Springer, 2007.
doi: 10.1007/978-0-387-39351-3. |
[21] |
R. B. Lehoucq, D. C. Sorensen and C. Yang, ARPACK Users' Guide: Solution of Large-Scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods, volume 6. SIAM, 1998.
doi: 10.1137/1.9780898719628. |
[22] |
A. Mauroy and I. Mezic, A spectral operator-theoretic framework for global stability, In 52nd IEEE Conference on Decision and Control, (2013), 5234-5239.
doi: 10.1109/CDC.2013.6760712. |
[23] |
I. Mezić, Spectral properties of dynamical systems, model reduction and decompositions, Nonlinear Dynamics, 41 (2005), 309-325.
doi: 10.1007/s11071-005-2824-x. |
[24] |
I. Mezić, Analysis of fluid flows via spectral properties of the Koopman operator, Annual Review of Fluid Mechanics, 45 (2013), 357-378.
doi: 10.1146/annurev-fluid-011212-140652. |
[25] |
C. E. Rasmussen, Gaussian Processes for Machine Learning, MIT Press, 2006. |
[26] |
C. W. Rowley, I. Mezić, S. Bagheri, P. Schlatter and D. S. Henningson, Spectral analysis of nonlinear flows, Journal of Fluid Mechanics, 641 (2009), 115-127.
doi: 10.1017/S0022112009992059. |
[27] |
P. J. Schmid, Dynamic mode decomposition of numerical and experimental data, Journal of Fluid Mechanics, 656 (2010), 5-28.
doi: 10.1017/S0022112010001217. |
[28] |
P. J. Schmid, D. Violato and F. Scarano, Decomposition of time-resolved tomographic PIV, Experiments in Fluids, 52 (2012), 1567-1579.
doi: 10.1007/s00348-012-1266-8. |
[29] |
P. J. Schmid, L. Li, M. P. Juniper and O. Pust, Applications of the dynamic mode decomposition, Theoretical and Computational Fluid Dynamics, 25 (2011), 249-259.
doi: 10.1007/s00162-010-0203-9. |
[30] |
B. Scholkopf, The kernel trick for distances, Advances in Neural Information Processing Systems, (2001), 301-307. |
[31] |
L. Sirovich, Turbulence and the dynamics of coherent structures. part I: Coherent structures, Quarterly of applied mathematics, 45 (1987), 561-571. |
[32] |
G. Tissot, L. Cordier, N. Benard and B. R. Noack, Model reduction using dynamic mode decomposition, Comptes Rendus Mćcanique, 342 (2014), 410-416.
doi: 10.1016/j.crme.2013.12.011. |
[33] |
J. H. Tu, C. W. Rowley, J. N. Kutz and J. K. Shang, Toward compressed DMD: Spectral analysis of fluid flows using sub-Nyquist-rate PIV data, Experiments in Fluids, 55 (2014), 1-13. |
[34] |
J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton and J. N. Kutz, On dynamic mode decomposition: Theory and applications, Journal of Computational Dynamics, 1 (2014), 391-421.
doi: 10.3934/jcd.2014.1.391. |
[35] |
M. O. Williams, I. G. Kevrekidis and C. W. Rowley, A data-driven approximation of the Koopman operator: Extending dynamic mode decomposition, Journal of Nonlinear Science, 25 (2015), 1307-1346.
doi: 10.1007/s00332-015-9258-5. |
[36] |
A. Wynn, D. S. Pearson, B. Ganapathisubramani and P. J. Goulart, Optimal mode decomposition for unsteady flows, Journal of Fluid Mechanics, 733 (2013), 473-503.
doi: 10.1017/jfm.2013.426. |
show all references
References:
[1] |
S. Bagheri, Koopman-mode decomposition of the cylinder wake, Journal of Fluid Mechanics, 726 (2013), 596-623.
doi: 10.1017/jfm.2013.249. |
[2] |
S. Bagheri, Effects of weak noise on oscillating flows: Linking quality factor, Floquet modes, and Koopman spectrum, Physics of Fluids, 26 (2014), 094104.
doi: 10.1063/1.4895898. |
[3] |
G. Baudat and F. Anouar, Kernel-based methods and function approximation, In Proceedings of the International Joint Conference on Neural Networks, IEEE, 2 (2001), 1244-1249.
doi: 10.1109/IJCNN.2001.939539. |
[4] |
C. M. Bishop et al, Pattern Recognition and Machine Learning, Springer, 2006.
doi: 10.1007/978-0-387-45528-0. |
[5] |
J. P. Boyd, Chebyshev and Fourier Spectral Methods, Courier Dover Publications, Mineola, NY, 2001. |
[6] |
M. Budišić, R. Mohr and I. Mezić, Applied Koopmanism, Chaos: An Interdisciplinary Journal of Nonlinear Science, 22 (2012), 047510, 33pp.
doi: 10.1063/1.4772195. |
[7] |
C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2 (1998), 121-167. |
[8] |
A. Chatterjee, An introduction to the proper orthogonal decomposition, Current Science, 78 (2000), 808-817. |
[9] |
K. K. Chen, J. H. Tu and C. W. Rowley, Variants of dynamic mode decomposition: Boundary condition, Koopman, and Fourier analyses, Journal of Nonlinear Science, 22 (2012), 887-915.
doi: 10.1007/s00332-012-9130-9. |
[10] |
R. R. Coifman and S. Lafon, Diffusion maps, Applied and Computational Harmonic Analysis, 21 (2006), 5-30.
doi: 10.1016/j.acha.2006.04.006. |
[11] |
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
doi: 10.1017/CBO9780511801389. |
[12] |
C. E. Elmer and E. S. Van Vleck, Spatially discrete FitzHugh-Nagumo equations, SIAM Journal on Applied Mathematics, 65 (2005), 1153-1174.
doi: 10.1137/S003613990343687X. |
[13] |
P. Gaspard and S. Tasaki, Liouvillian dynamics of the {Hopf} bifurcation, Physical Review E, 64 (2001), 056232, 17pp.
doi: 10.1103/PhysRevE.64.056232. |
[14] |
M. S. Hemati, M. O. Williams and C. W. Rowley, Dynamic mode decomposition for large and streaming datasets, Physics of Fluids, 26 (2014), 111701.
doi: 10.1063/1.4901016. |
[15] |
P. Holmes, J. L. Lumley, G. Berkooz and C. W. Rowley, Turbulence, Coherent Structures, Dynamical Systems and Symmetry, Cambridge University Press, 2nd edition, 2012.
doi: 10.1017/CBO9780511919701. |
[16] |
M. R. Jovanović, P. J. Schmid and J. W. Nichols, Sparsity-promoting dynamic mode decomposition, Physics of Fluids, 26 (2014), 024103. |
[17] |
J.-N. Juang, Applied System Identification, Prentice Hall, 1994. |
[18] |
B. O. Koopman and J. von Neumann, Dynamical systems of continuous spectra, Proceedings of the National Academy of Sciences of the United States of America, 18 (1932), 255-163.
doi: 10.1073/pnas.18.3.255. |
[19] |
B. O. Koopman, Hamiltonian systems and transformation in Hilbert space, Proceedings of the National Academy of Sciences of the United States of America, 17 (1931), 315-318.
doi: 10.1073/pnas.17.5.315. |
[20] |
J. A. Lee and M. Verleysen, Nonlinear Dimensionality Reduction, Springer, 2007.
doi: 10.1007/978-0-387-39351-3. |
[21] |
R. B. Lehoucq, D. C. Sorensen and C. Yang, ARPACK Users' Guide: Solution of Large-Scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods, volume 6. SIAM, 1998.
doi: 10.1137/1.9780898719628. |
[22] |
A. Mauroy and I. Mezic, A spectral operator-theoretic framework for global stability, In 52nd IEEE Conference on Decision and Control, (2013), 5234-5239.
doi: 10.1109/CDC.2013.6760712. |
[23] |
I. Mezić, Spectral properties of dynamical systems, model reduction and decompositions, Nonlinear Dynamics, 41 (2005), 309-325.
doi: 10.1007/s11071-005-2824-x. |
[24] |
I. Mezić, Analysis of fluid flows via spectral properties of the Koopman operator, Annual Review of Fluid Mechanics, 45 (2013), 357-378.
doi: 10.1146/annurev-fluid-011212-140652. |
[25] |
C. E. Rasmussen, Gaussian Processes for Machine Learning, MIT Press, 2006. |
[26] |
C. W. Rowley, I. Mezić, S. Bagheri, P. Schlatter and D. S. Henningson, Spectral analysis of nonlinear flows, Journal of Fluid Mechanics, 641 (2009), 115-127.
doi: 10.1017/S0022112009992059. |
[27] |
P. J. Schmid, Dynamic mode decomposition of numerical and experimental data, Journal of Fluid Mechanics, 656 (2010), 5-28.
doi: 10.1017/S0022112010001217. |
[28] |
P. J. Schmid, D. Violato and F. Scarano, Decomposition of time-resolved tomographic PIV, Experiments in Fluids, 52 (2012), 1567-1579.
doi: 10.1007/s00348-012-1266-8. |
[29] |
P. J. Schmid, L. Li, M. P. Juniper and O. Pust, Applications of the dynamic mode decomposition, Theoretical and Computational Fluid Dynamics, 25 (2011), 249-259.
doi: 10.1007/s00162-010-0203-9. |
[30] |
B. Scholkopf, The kernel trick for distances, Advances in Neural Information Processing Systems, (2001), 301-307. |
[31] |
L. Sirovich, Turbulence and the dynamics of coherent structures. part I: Coherent structures, Quarterly of applied mathematics, 45 (1987), 561-571. |
[32] |
G. Tissot, L. Cordier, N. Benard and B. R. Noack, Model reduction using dynamic mode decomposition, Comptes Rendus Mćcanique, 342 (2014), 410-416.
doi: 10.1016/j.crme.2013.12.011. |
[33] |
J. H. Tu, C. W. Rowley, J. N. Kutz and J. K. Shang, Toward compressed DMD: Spectral analysis of fluid flows using sub-Nyquist-rate PIV data, Experiments in Fluids, 55 (2014), 1-13. |
[34] |
J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brunton and J. N. Kutz, On dynamic mode decomposition: Theory and applications, Journal of Computational Dynamics, 1 (2014), 391-421.
doi: 10.3934/jcd.2014.1.391. |
[35] |
M. O. Williams, I. G. Kevrekidis and C. W. Rowley, A data-driven approximation of the Koopman operator: Extending dynamic mode decomposition, Journal of Nonlinear Science, 25 (2015), 1307-1346.
doi: 10.1007/s00332-015-9258-5. |
[36] |
A. Wynn, D. S. Pearson, B. Ganapathisubramani and P. J. Goulart, Optimal mode decomposition for unsteady flows, Journal of Fluid Mechanics, 733 (2013), 473-503.
doi: 10.1017/jfm.2013.426. |
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