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Recommendations for the article Deep learning for universal linear embeddings of nonlinear dynamics

Abstract	Title	Authors	Publication Date	Journal/ Conference	Citation count	Highest h-index
visibility_off	Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data	Anthony Frion, Lucas Drumetz, M. Mura, G. Tochon, Abdeldjalil Aissa El Bey	2023-03-13	ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)	4	34
visibility_off	Modern Koopman Theory for Dynamical Systems	S. Brunton, M. Budišić, E. Kaiser, J. Kutz	2021-02-24	SIAM Rev.	271	63
visibility_off	Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics	P. Goyal, Süleyman Yıldız, P. Benner	2023-08-26	ArXiv	0	53
visibility_off	Autoencoding for the 'Good Dictionary' of eigen pairs of the Koopman Operator	Neranjaka Jayarathne, E. Bollt	2023-06-08	ArXiv	0	37
visibility_off	Deep learning for Koopman Operator Optimal Control.	Mostafa Al‐Gabalawy	2021-01-06	ISA transactions	13	11
visibility_off	Generalized Quadratic-Embeddings for Nonlinear Dynamics using Deep Learning	P. Goyal, P. Benner	2022-11-01	ArXiv	7	53
visibility_off	Learned Lifted Linearization Applied to Unstable Dynamic Systems Enabled by Koopman Direct Encoding	Jerry Ng, H. Asada	2022-10-24	IEEE Control Systems Letters	1	12
visibility_off	Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems	Enoch Yeung, Soumya Kundu, Nathan Oken Hodas	2017-08-22	2019 American Control Conference (ACC)	326	17
visibility_off	DLKoopman: A deep learning software package for Koopman theory	Sourya Dey, Eric K. Davis	2022-11-15	DBLP, ArXiv	2	6
Abstract	Title	Authors	Publication Date	Journal/Conference	Citation count	Highest h-index