Dynamical systems and complex networks: a Koopman operator perspective by Stefan Klus, Nataša Djurdjevac Conrad

Subjects: “…Koopman operator…”
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Data-driven Static Equivalence with Physics-informed Koopman Operators by Wei Lin, Changhong Zhao, Maosheng Gao, C. Y. Chung

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A quantitative analysis of Koopman operator methods for system identification and predictions by Zhang, Christophe, Zuazua, Enrique

Subjects: “…Koopman operator…”
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Asymptotically stable data-driven koopman operator approximation with inputs using total extended DMD by Louis Lortie, James Richard Forbes

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Data-Driven Identification of Gas Turbine Engine Dynamics via Koopman Operator Genetic Algorithm by David Grasev

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Short-Term Voltage Stability Prediction for Power Systems Based on a Dominant Koopman Operator-Enhanced MLE by Han Gao, Deyou Yang, Yanling Lv, Lixin Wang

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A Data-Driven Observer for Wind Farm Power Gain Potential: A Sparse Koopman Operator Approach by Yue Chen, Bingchen Wang, Kaiyue Zeng, Lifu Ding, Yingming Lin, Ying Chen, Qiuyu Lu

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From Block-Oriented Models to the Koopman Operator: A Comprehensive Review on Data-Driven Chemical Reactor Modeling by Mustapha Kamel Khaldi, Mujahed Al-Dhaifallah, Ibrahim Aljamaan, Fouad Mohammad Al-Sunni, Othman Taha, Abdullah Alharbi

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Online Tuning of Koopman Operator for Fault-Tolerant Control: A Case Study of Mobile Robot Localising on Minimal Sensor Information by Ravi Kiran Akumalla, Tushar Jain

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Koopman-Driven Grip Force Prediction Through EMG Sensing by Tomislav Bazina, Ervin Kamenar, Maria Fonoberova, Igor Mezic

Subjects: “…Koopman operator theory…”
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An Alternative Framework for Dynamic Mode Decomposition with Control by Gyurhan Nedzhibov

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Koopman-based linearization of preparatory EEG dynamics in Parkinson’s disease during galvanic vestibular stimulation by Maryam Kia, Maryam Kia, Maryam S. Mirian, Saeed Soori, Saeed Saedi, Emad Arasteh, Mohamad Hosein Faramarzi, Abhijit Chinchani, Soojin Lee, Artur Luczak, Martin J. McKeown

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Deep learning for structure-preserving universal stable Koopman-inspired embeddings for nonlinear canonical Hamiltonian dynamics by Pawan Goyal, Süleyman Yıldız, Peter Benner

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Recursive Forward-Backward EDMD: Guaranteed Algebraic Search for Koopman Invariant Subspaces by Masih Haseli, Jorge Cortes

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Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems by J. Nathan Kutz, J. L. Proctor, S. L. Brunton

“…We demonstrate that the observables chosen for constructing the Koopman operator are critical for enabling an accurate approximation to the nonlinear dynamics. …”
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Optimal DMD Koopman Data-Driven Control of a Worm Robot by Mehran Rahmani, Sangram Redkar

“…The dynamic mode decomposition (DMD) method is used to generate the Koopman operator. Finally, a linear quadratic regulator (LQR) control method is applied for the control of the worm robot. …”
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On Prigogine's approaches to irreversibility: a case study by the baker map by S. Tasaki

“…In both theories, the evolution operator U† of observables (the Koopman operator) is found to acquire dissipativity by restricting observables to an appropriate subspace Φ of the Hilbert space L2 of square integrable functions. …”
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Deciphering chaos in the Madden-Julian oscillation by Guosen Chen

“…Combining eigen-time-delay embedding and Koopman operator into a regression model, we further reveal the cause of chaos through a data-drive approach. …”
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Recursive regulator: a deep-learning and real-time model adaptation strategy for nonlinear systems by Jinming Sun, Yanqiu Huang, Wanli Yu, Alberto Garcia-Ortiz

“…To address this challenge, here we present a strategy that applies a regulator to the Koopman operator, enabling real-time model adaptation for nonlinear systems. …”
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Temporally-consistent koopman autoencoders for forecasting dynamical systems by Indranil Nayak, Ananda Chakrabarti, Mrinal Kumar, Fernando L. Teixeira, Debdipta Goswami

“…Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-order feature space with simpler, linear dynamics. …”
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