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Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems.

, , and . CoRR, (2017)

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A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems., , and . CoRR, (2017)Representing structure in linear interconnected dynamical systems., , , and . CDC, page 6010-6015. IEEE, (2010)Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems., , , and . CDC, page 8106-8111. IEEE, (2019)Resource competition as a source of non-minimum phase behavior in transcription-translation systems., , and . CDC, page 4060-4067. IEEE, (2013)Data Driven Online Learning of Power System Dynamics., , and . CoRR, (2020)Data-Driven Operator Theoretic Methods for Phase Space Learning and Analysis., , and . CoRR, (2021)Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition., , , and . ACC, page 4245-4251. IEEE, (2020)Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach., , and . SmartGridComm, page 1-6. IEEE, (2020)A Class of Logistic Functions for Approximating State-Inclusive Koopman Operators., and . ACC, page 4803-4810. IEEE, (2018)Contingency analysis of water distribution networks using quadratic sensitivity functions., , and . ACC, page 228-233. IEEE, (2017)