J. Nathan Kutz

29.8k total citations · 15 hit papers
341 papers, 18.2k citations indexed

About

J. Nathan Kutz is a scholar working on Statistical and Nonlinear Physics, Atomic and Molecular Physics, and Optics and Electrical and Electronic Engineering. According to data from OpenAlex, J. Nathan Kutz has authored 341 papers receiving a total of 18.2k indexed citations (citations by other indexed papers that have themselves been cited), including 151 papers in Statistical and Nonlinear Physics, 135 papers in Atomic and Molecular Physics, and Optics and 96 papers in Electrical and Electronic Engineering. Recurrent topics in J. Nathan Kutz's work include Advanced Fiber Laser Technologies (116 papers), Model Reduction and Neural Networks (85 papers) and Laser-Matter Interactions and Applications (58 papers). J. Nathan Kutz is often cited by papers focused on Advanced Fiber Laser Technologies (116 papers), Model Reduction and Neural Networks (85 papers) and Laser-Matter Interactions and Applications (58 papers). J. Nathan Kutz collaborates with scholars based in United States, United Kingdom and Hong Kong. J. Nathan Kutz's co-authors include Steven L. Brunton, Joshua L. Proctor, Bingni W. Brunton, Samuel Rudy, Bethany Lusch, Jared C. Bronski, Bernard Deconinck, Lincoln D. Carr, Eurika Kaiser and Xing Fu and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

J. Nathan Kutz

329 papers receiving 17.6k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

2016 2.6k citations Steven L. Brunton, J. Nathan Kutz et al. Proceedings of the National Academy of Sciences profile →
Data-driven discovery of partial differential equations

2017 931 citations Samuel Rudy, Steven L. Brunton et al. profile →
Deep learning for universal linear embeddings of nonlinear dynamics

2018 793 citations J. Nathan Kutz, Steven L. Brunton et al. Nature Communications profile →
Data-Driven Science and Engineering

2019 710 citations Steven L. Brunton, J. Nathan Kutz profile →
Dynamic Mode Decomposition

2016 689 citations J. Nathan Kutz, Steven L. Brunton et al. profile →
Dynamic Mode Decomposition with Control

2016 611 citations Steven L. Brunton, J. Nathan Kutz et al. profile →
Deep learning in fluid dynamics

2017 564 citations J. Nathan Kutz profile →
Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems

2016 434 citations J. Nathan Kutz, Steven L. Brunton et al. profile →
Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control

2016 355 citations Steven L. Brunton, Bingni W. Brunton et al. profile →
Chaos as an intermittently forced linear system

2017 343 citations Steven L. Brunton, Bingni W. Brunton et al. Nature Communications profile →
Modern Koopman Theory for Dynamical Systems

2022 257 citations Steven L. Brunton, Eurika Kaiser et al. profile →
Generalizing Koopman Theory to Allow for Inputs and Control.

2018 233 citations Steven L. Brunton, J. Nathan Kutz et al. profile →
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control

2022 167 citations J. Nathan Kutz, Bingni W. Brunton et al. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences profile →
PySINDy: A comprehensive Python package for robust sparse system identification

2022 105 citations Alan A. Kaptanoglu, Brian M. de Silva et al. The Journal of Open Source Software profile →
Physics-informed dynamic mode decomposition

2023 87 citations J. Nathan Kutz, Steven L. Brunton et al. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences profile →

Peers

Countries citing papers authored by J. Nathan Kutz

Since Specialization

Citations

This map shows the geographic impact of J. Nathan Kutz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by J. Nathan Kutz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Nathan Kutz more than expected).

Fields of papers citing papers by J. Nathan Kutz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Nathan Kutz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by J. Nathan Kutz. The network helps show where J. Nathan Kutz may publish in the future.

Co-authorship network of co-authors of J. Nathan Kutz

This figure shows the co-authorship network connecting the top 25 collaborators of J. Nathan Kutz. A scholar is included among the top collaborators of J. Nathan Kutz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with J. Nathan Kutz. J. Nathan Kutz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Reduced order modeling with shallow recurrent decoder networks 2025 ·Nature Communications ·(unknown), (unknown), Francesco Braghin, Andrea Manzoni, J. Nathan Kutz	0
2	Automating the practice of science: Opportunities, challenges, and implications 2025 ·Proceedings of the National Academy of Sciences ·Sebastian Musslick, (unknown), (unknown), (unknown), Fernand Gobet, Thomas L. Griffiths, Jessica Hullman, Ross D. King, J. Nathan Kutz, Christopher G. Lucas, Suhas Mahesh, Franco Pestilli, (unknown), William R. Holmes	7
3	Data-driven local operator finding for reduced-order modeling of plasma systems 2025 ·Journal of Physics D Applied Physics ·Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz	3
4	Arousal as a universal embedding for spatiotemporal brain dynamics 2025 ·Nature ·Ryan V. Raut, Zachary P. Rosenthal, Xiaodan Wang, (unknown), (unknown), Jin‐Moo Lee, Marcus E. Raichle, Adam Q. Bauer, Steven L. Brunton, Bingni W. Brunton, J. Nathan Kutz	2
5	Nonlinear parametric models of viscoelastic fluid flows 2024 ·Royal Society Open Science ·Cássio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz, Steven L. Brunton	3
6	Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics 2024 ·IEEE Transactions on Computational Imaging ·Aleksei Sholokhov, Saleh Nabi, Joshua Rapp, Steven L. Brunton, J. Nathan Kutz, Petros T. Boufounos, Hassan Mansour	1
7	PyKoopman: A Python Package for Data-DrivenApproximation of the Koopman Operator 2024 ·The Journal of Open Source Software ·Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton	7
8	Multi-fidelity reduced-order surrogate modelling 2024 ·Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences ·(unknown), Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz	17
9	A machine learning approach to quantify individual gait responses to ankle exoskeletons 2023 ·Journal of Biomechanics ·(unknown), (unknown), J. Nathan Kutz, Katherine M. Steele	6
10	The functional impact of 1,570 individual amino acid substitutions in human OTC 2023 ·The American Journal of Human Genetics ·Russell S. Lo, Gareth A. Cromie, Michelle Tang, (unknown), (unknown), Amy Sirr, J. Nathan Kutz, Hiroki Morizono, Ljubica Caldovic, Nicholas Ah Mew, Andrea Gropman, Aimée M. Dudley	10
11	Dynamic mode decomposition for data-driven analysis and reduced-order modeling of E × B plasmas: I. Extraction of spatiotemporally coherent patterns 2023 ·Journal of Physics D Applied Physics ·Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz	14
12	Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy ^* 2023 ·Nuclear Fusion ·J. Lore, S. De Pascuale, (unknown), (unknown), Jae-Sun Park, J.M. Park, Steven L. Brunton, J. Nathan Kutz, Alan A. Kaptanoglu	14
13	Dynamic mode decomposition for data-driven analysis and reduced-order modeling of E × B plasmas: II. Dynamics forecasting 2023 ·Journal of Physics D Applied Physics ·Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz	11
14	Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight 2022 ·PLoS Computational Biology ·(unknown), (unknown), Callin M. Switzer, Thomas L. Daniel, J. Nathan Kutz	3
15	Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings 2020 ·Journal of The Royal Society Interface ·(unknown), Manuel Zimmer, J. Nathan Kutz	9
16	Data-driven identification of parametric partial differential equations 2019 ·IRIS Research product catalog (Sapienza University of Rome) ·Samuel Rudy, Alessandro Alla, Steven L. Brunton, J. Nathan Kutz	190
17	Data-driven science and engineering : machine learning, dynamical systems, and control 2019 ·Virtual Defense Library (Ministerio de Defensa) ·Steven L. Brunton, J. Nathan Kutz	149
18	Randomized nonnegative matrix factorization 2018 ·Pattern Recognition Letters ·N. Benjamin Erichson, (unknown), (unknown), J. Nathan Kutz	28
19	Nonlinear model order reduction via dynamic mode decomposition 2017 ·IRIS Research product catalog (Sapienza University of Rome) ·Alessandro Alla, J. Nathan Kutz	59
20	Multi-Resolution Analysis of Dynamical Systems using Dynamic Mode Decomposition 2015 ·Lecture notes in computer science ·J. Nathan Kutz, Steven L. Brunton, Xing Fu	2

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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