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The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep state-space encoders.
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Williams, M.O., Kevrekidis, I.G. and Rowley, C.W., ”A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition,” Journal of Nonlinear Science
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Brunton, S.L., Brunton, B.W., Proctor, J.L. and Kutz, J.N., ”Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control,” The Public Library of Science ONE
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Takeishi, N., Kawahara, Y. and Yairi, T., ”Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition,” International Conference on Neural Information Processing Systems (NIPS)
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Lusch, B., Kutz, J.N. and Brunton, S.L., ”Deep learning for universal linear embeddings of nonlinear dynamics,” Nature Communications,
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Korda, M. and Mezic, I., “Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control,” Automatica
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Mauroy, A., Mezić, I. and Susuki, Y., The Koopman Operator in Systems and Control
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Bonnert, M. and Konigorski, U., ”Estimating Koopman Invariant Subspaces of Excited Systems Using Artificial Neural Networks,” IFAC-PapersOnLine
2020
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2020
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Mauroy, A. and Goncalves, J., ”Koopman-Based Lifting Techniques for Nonlinear Systems Identification,” IEEE Transactions on Automatic Control
2020
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Beintema, G., Tóth, R. and Schoukens., M., “Nonlinear state-space identification using deep encoder networks,” Proceedings of Learning for Dynamics and Control (L4DC)
2021
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2019
Cited alongside, same era.
Otto, S.E. and Rowley, C.W., ”Linearly Recurrent Autoencoder Networks for Learning Dynamics”, SIAM Journal on Applied Dynamical Systems
2019
Cited alongside, same era.
Yeung, E., Kundu, S. and Hodas, N.O., “Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems,” American Control Conference (ACC)
2019
Cited alongside, same era.
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Beintema, G., Tóth, R. and Schoukens., M., “Non-linear State-space Model Identification from Video Data using Deep Encoders,” 19th IFAC Symposium on System Identification (SYSID)
2021
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