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D-Index & Metrics

Engineering and Technology

D-Index
85
Citations
37645
World Ranking
378
National Ranking
125

Overview

J. Nathan Kutz is affiliated with the University of Washington in the United States. Their research spans multiple fields, primarily focused on Engineering and Physics and Astronomy. They contribute extensively to the subfields of Statistical and Nonlinear Physics, Artificial Intelligence, Control and Systems Engineering, Computational Mechanics, and Statistics, Probability and Uncertainty.

Their work encompasses a range of interconnected topics including Model Reduction and Neural Networks, Probabilistic and Robust Engineering Design, Fluid Dynamics and Turbulent Flows, Control Systems and Identification, Fault Detection and Control Systems, Cardiac Arrest and Resuscitation, as well as Fluid Dynamics and Vibration Analysis.

J. Nathan Kutz has published prolifically, with frequent contributions appearing in these venues:

  • arXiv (Cornell University)
  • Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • IEEE Access
  • Zenodo (CERN European Organization for Nuclear Research)
  • Circulation

Recent papers include:

  • Modern Koopman Theory for Dynamical Systems, 2022, SIAM Review
  • Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control, 2022, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics, 2020, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • Shallow neural networks for fluid flow reconstruction with limited sensors, 2020, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data, 2020, The Journal of Open Source Software

The scientist has collaborated frequently with a core group of coauthors, including Steven L. Brunton, Bingni W. Brunton, Heemun Kwok, Jason Coult, and Thomas D. Rea.

J. Nathan Kutz is also an author of books published by Springer Nature, including a recent work titled Model Order Reduction and Applications (2023).

Best Publications

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

    Steven L. Brunton;Joshua L. Proctor;J. Nathan Kutz

  • On dynamic mode decomposition: Theory and applications

    Jonathan H. Tu;Clarence Worth Rowley;Dirk M. Luchtenburg;Steven L. Brunton

  • Data-driven discovery of partial differential equations.

    Samuel H. Rudy;Steven L. Brunton;Joshua L. Proctor;J. Nathan Kutz

  • Deep learning for universal linear embeddings of nonlinear dynamics.

    Bethany Lusch;J. Nathan Kutz;Steven L. Brunton

  • Dynamic Mode Decomposition with Control

    Joshua L. Proctor;Steven L. Brunton;J. Nathan Kutz

  • Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems

    J. Nathan Kutz;Steven L. Brunton;Bingni W. Brunton;Joshua L. Proctor

  • Deep learning in fluid dynamics

    J. Nathan Kutz

  • Data-driven discovery of coordinates and governing equations

    Kathleen Champion;Bethany Lusch;J. Nathan Kutz;Steven L. Brunton

  • Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control

    Steven L. Brunton;Bingni W. Brunton;Joshua L. Proctor;J. Nathan Kutz

  • Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

    Steven L. Brunton;J. Nathan Kutz

  • Chaos as an intermittently forced linear system.

    Steven L. Brunton;Bingni W. Brunton;Joshua L. Proctor;Eurika Kaiser

  • Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

    Bingni W. Brunton;Lise A. Johnson;Jeffrey G. Ojemann;J. Nathan Kutz

  • Sparse identification of nonlinear dynamics for model predictive control in the low-data limit.

    E. Kaiser;J. N. Kutz;S. L. Brunton

  • Data-Driven Sparse Sensor Placement for Reconstruction

    Krithika Manohar;Bingni W. Brunton;J. Nathan Kutz;Steven L. Brunton

  • Multiresolution Dynamic Mode Decomposition

    J. Nathan Kutz;Xing Fu;Steven L. Brunton

  • Modern Koopman Theory for Dynamical Systems.

    Steven L. Brunton;Marko Budisic;Eurika Kaiser;J. Nathan Kutz

  • Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

    Eurika Kaiser;J. Nathan Kutz;Steven Brunton

  • Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

    Krithika Manohar;Bingni W. Brunton;J. Nathan Kutz;Steven L. Brunton

  • Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics

    Niall M. Mangan;Steven L. Brunton;Joshua L. Proctor;J. Nathan Kutz

  • Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data

    J. Nathan Kutz

  • Bose-Einstein condensates in standing waves: the cubic nonlinear Schrödinger equation with a periodic potential.

    Jared C. Bronski;Lincoln D. Carr;Bernard Deconinck;J. Nathan Kutz

  • Neural Networks and Deep Learning

    Steven L. Brunton;J. Nathan Kutz

Frequent Co-Authors

Steven T. Cundiff
Steven T. Cundiff University of Michigan–Ann Arbor
Ping-kong Alexander Wai
Ping-kong Alexander Wai Hong Kong Polytechnic University
Herbert G. Winful
Herbert G. Winful University of Michigan–Ann Arbor
Frank W. Wise
Frank W. Wise Cornell University
Roberto Morandotti
Roberto Morandotti Institut National de la Recherche Scientifique
Demetrios N. Christodoulides
Demetrios N. Christodoulides University of Southern California
Richard P. Mirin
Richard P. Mirin National Institute of Standards and Technology
Clarence W. Rowley
Clarence W. Rowley Princeton University
Keren Bergman
Keren Bergman Columbia University
William L. Kath
William L. Kath Northwestern University

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