J. Nathan Kutz

ORCID

Publication Activity (10 Years)

Years Active: 1996-2024
Publications (10 Years): 179

Top Venues

CoRR

SIAM J. Appl. Dyn. Syst.

IEEE Access

Frontiers Comput. Neurosci.

Publications

Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz
Data-driven local operator finding for reduced-order modelling of plasma systems: II. Application to parametric dynamics. CoRR (2024)
Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. CoRR (2024)
Steven L. Brunton, J. Nathan Kutz
Promising directions of machine learning for partial differential equations. Nat. Comput. Sci. 4 (7) (2024)
Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz
Data-driven local operator finding for reduced-order modelling of plasma systems: I. Concept and verifications. CoRR (2024)
Megan Morrison, J. Nathan Kutz
Solving Nonlinear Ordinary Differential Equations Using the Invariant Manifolds and Koopman Eigenfunctions. SIAM J. Appl. Dyn. Syst. 23 (1) (2024)
Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. J. Open Source Softw. 9 (96) (2024)
Olivia T. Zahn, Thomas L. Daniel, J. Nathan Kutz
Motif distribution and function of sparse deep neural networks. CoRR (2024)
Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Brunton
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning. CoRR (2024)
Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz, Krithika Manohar
Statistical Mechanics of Dynamical System Identification. CoRR (2024)
Megan R. Ebers, Jan P. Williams, Katherine M. Steele, J. Nathan Kutz
Leveraging Arbitrary Mobile Sensor Trajectories With Shallow Recurrent Decoder Networks for Full-State Reconstruction. IEEE Access 12 (2024)
J. Nathan Kutz, Maryam Reza, Farbod Faraji, Aaron Knoll
Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics. CoRR (2024)
Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, J. Nathan Kutz
PyDMD: A Python package for robust dynamic mode decomposition. CoRR (2024)
J. Nathan Kutz, Steven L. Brunton, Krithika Manohar, Hod Lipson, Na Li
AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data-driven control. AI Mag. 45 (1) (2024)
Jiazhong Mei, Steven L. Brunton, J. Nathan Kutz
Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation. Sensors 24 (12) (2024)
Aleksei Sholokhov, Saleh Nabi, Joshua Rapp, Steven L. Brunton, J. Nathan Kutz, Petros T. Boufounos, Hassan Mansour
Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics. IEEE Trans. Computational Imaging 10 (2024)
Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks. CoRR (2024)
Aleksei Sholokhov, Joshua Rapp, Saleh Nabi, Steven L. Brunton, J. Nathan Kutz, Hassan Mansour
Single-Pixel Imaging Of Dynamic Flows Using Neural Ode Regularization. ICASSP (2024)
Meghana Velegar, Christoph Keller, J. Nathan Kutz
Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data. CoRR (2024)
Olga Dorabiala, Aleksandr Y. Aravkin, J. Nathan Kutz
Ensemble Principal Component Analysis. IEEE Access 12 (2024)
Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz
Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects. SIAM J. Appl. Dyn. Syst. 23 (1) (2024)
Erfan Abbasgholinejad, Haoqin Deng, John King Gamble, J. Nathan Kutz, Erik Nielsen, Neal C. Pisenti, Ningzhi Xie
Extremum Seeking Control of Quantum Gates. QCE (2023)
Erdi Kara, George Zhang, Joseph J. Williams, Gonzalo Ferrandez-Quinto, Leviticus J. Rhoden, Maximilian Kim, J. Nathan Kutz, Aminur Rahman
Deep Learning Based Object Tracking in Walking Droplet and Granular Intruder Experiments. CoRR (2023)
Aminur Rahman, J. Nathan Kutz
Walking Droplets as a Damped-Driven System. SIAM J. Appl. Dyn. Syst. 22 (2) (2023)
Shaowu Pan, Steven L. Brunton, J. Nathan Kutz
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. J. Mach. Learn. Res. 24 (2023)
L. Mars Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery. CoRR (2023)
Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC. ICRA (2023)
Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz, Steven L. Brunton
Nonlinear parametric models of viscoelastic fluid flows. CoRR (2023)
Mozes Jacobs, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz, Ryan V. Raut
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations. CoRR (2023)
Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. CoRR (2023)
Ziyu Lu, Anika Tabassum, Shruti R. Kulkarni, Lu Mi, J. Nathan Kutz, Eric Shea-Brown, Seung-Hwan Lim
Attention for Causal Relationship Discovery from Biological Neural Dynamics. CoRR (2023)
Megan R. Ebers, Jan P. Williams, Katherine M. Steele, J. Nathan Kutz
Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction. CoRR (2023)
Katherine Owens, J. Nathan Kutz
Data-Driven Discovery of Governing Equations for Coarse-Grained Heterogeneous Network Dynamics. SIAM J. Appl. Dyn. Syst. 22 (3) (2023)
Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz
Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: II. dynamics forecasting. CoRR (2023)
Megan Morrison, J. Nathan Kutz, Michael Gabbay
Transitions between peace and systemic war as bifurcations in a signed network dynamical system. Netw. Sci. 11 (3) (2023)
John Ferré, Ariel Rokem, Elizabeth A. Buffalo, J. Nathan Kutz, Adrienne Fairhall
Non-Stationary Dynamic Mode Decomposition. IEEE Access 11 (2023)
Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz
Multi-fidelity reduced-order surrogate modeling. CoRR (2023)
Jiazhong Mei, J. Nathan Kutz, Steven L. Brunton
Observability-Based Energy Efficient Path Planning with Background Flow via Deep Reinforcement Learning. CDC (2023)
Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar
Data-Induced Interactions of Sparse Sensors. CoRR (2023)
Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz
Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: I. Extraction of spatiotemporally coherent patterns. CoRR (2023)
Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning. CoRR (2023)
Bian Li, Yi-An Ma, J. Nathan Kutz, Xiu Yang
The Adaptive Spectral Koopman Method for Dynamical Systems. SIAM J. Appl. Dyn. Syst. 22 (3) (2023)
Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz
Multiresolution convolutional autoencoders. J. Comput. Phys. 474 (2023)
Erdi Kara, George Zhang, Joseph J. Williams, Gonzalo Ferrandez-Quinto, Leviticus J. Rhoden, Maximilian Kim, J. Nathan Kutz, Aminur Rahman
Deep learning based object tracking in walking droplet and granular intruder experiments. J. Real Time Image Process. 20 (5) (2023)
Steven L. Brunton, J. Nathan Kutz
Machine Learning for Partial Differential Equations. CoRR (2023)
Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz
Dimensionally Consistent Learning with Buckingham Pi. CoRR (2022)
Alex Mallen, Christoph A. Keller, J. Nathan Kutz
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data. CoRR (2022)
Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz
Modern Koopman Theory for Dynamical Systems. SIAM Rev. 64 (2) (2022)
Charles B. Delahunt, J. Nathan Kutz
A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes. IEEE Access 10 (2022)
Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé
Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 3 (1) (2022)
Joseph Bakarji, Kathleen P. Champion, J. Nathan Kutz, Steven L. Brunton
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders. CoRR (2022)
Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna Garikipati, Lori Graham-Brady, Amanda A. Howard, George Em Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, J. Nathan Kutz, Chunhui Li, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Hamdi A. Tchelepi, Bozo Vazic, Hari S. Viswanathan, Hongkyu Yoon, Piotr Zarzycki
Machine Learning in Heterogeneous Porous Materials. CoRR (2022)
Krithika Manohar, J. Nathan Kutz, Steven L. Brunton
Optimal Sensor and Actuator Selection Using Balanced Model Reduction. IEEE Trans. Autom. Control. 67 (4) (2022)
Olivia Zahn, Jorge Bustamante, Callin Switzer, Thomas L. Daniel, J. Nathan Kutz
Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight. PLoS Comput. Biol. 18 (9) (2022)
Olga Dorabiala, Jennifer Webster, J. Nathan Kutz, Aleksandr Y. Aravkin
Spatiotemporal k-means. CoRR (2022)
Travis Askham, Peng Zheng, Aleksandr Y. Aravkin, J. Nathan Kutz
Robust and Scalable Methods for the Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 21 (1) (2022)
Megan Morrison, J. Nathan Kutz, Michael Gabbay
Transitions between peace and systemic war as bifurcations in a signed network dynamical system. CoRR (2022)
Floris van Breugel, Yuying Liu, Bingni W. Brunton, J. Nathan Kutz
PyNumDiff: A Python package for numerical differentiation of noisy time-series data. J. Open Source Softw. 7 (71) (2022)
Nina de Lacy, Michael J. Ramshaw, J. Nathan Kutz
Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning. Frontiers Artif. Intell. 5 (2022)
Olga Dorabiala, J. Nathan Kutz, Aleksandr Y. Aravkin
Robust trimmed k-means. Pattern Recognit. Lett. 161 (2022)
Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC. CoRR (2022)
Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz
Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data. Mach. Learn. Sci. Technol. 3 (1) (2022)
Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz
Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems. IEEE Access 10 (2022)
Shaowu Pan, Steven L. Brunton, J. Nathan Kutz
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. CoRR (2022)
Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz
Dimensionally consistent learning with Buckingham Pi. Nat. Comput. Sci. 2 (12) (2022)
L. Mars Gao, J. Nathan Kutz
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. CoRR (2022)
Andy Goldschmidt, Jonathan L. Dubois, Steven L. Brunton, J. Nathan Kutz
Model predictive control for robust quantum state preparation. Quantum 6 (2022)
Kadierdan Kaheman, Urban Fasel, Jason J. Bramburger, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton
The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control. CoRR (2022)
Olga Dorabiala, J. Nathan Kutz, Aleksandr Y. Aravkin
Robust Trimmed k-means. CoRR (2021)
Urban Fasel, Eurika Kaiser, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton
SINDy with Control: A Tutorial. CDC (2021)
Charles B. Delahunt, J. Nathan Kutz
A toolkit for data-driven discovery of governing equations in high-noise regimes. CoRR (2021)
Manu Kalia, Steven L. Brunton, Hil G. E. Meijer, Christoph Brune, J. Nathan Kutz
Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations. CoRR (2021)
Megan Morrison, J. Nathan Kutz
Nonlinear Control of Networked Dynamical Systems. IEEE Trans. Netw. Sci. Eng. 8 (1) (2021)
Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton
Data-driven discovery of Koopman eigenfunctions for control. Mach. Learn. Sci. Technol. 2 (3) (2021)
Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Jared L. Callaham, Charles B. Delahunt, Kathleen P. Champion, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton
PySINDy: A comprehensive Python package for robust sparse system identification. CoRR (2021)
Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton
PySensors: A Python package for sparse sensor placement. J. Open Source Softw. 6 (58) (2021)
Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz
Modern Koopman Theory for Dynamical Systems. CoRR (2021)
Craig R. Gin, Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz
DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems. CoRR (2021)
Urban Fasel, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control. CoRR (2021)
Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz
Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data. IEEE Access 9 (2021)
Diya Sashidhar, J. Nathan Kutz
Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification. CoRR (2021)
Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz
PySensors: A Python Package for Sparse Sensor Placement. CoRR (2021)
Jason J. Bramburger, J. Nathan Kutz, Steven L. Brunton
Data-Driven Stabilization of Periodic Orbits. IEEE Access 9 (2021)
Alex Mallen, Henning Lange, J. Nathan Kutz
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties. CoRR (2021)
Peter J. Baddoo, Benjamin Herrmann, Beverley J. McKeon, J. Nathan Kutz, Steven L. Brunton
Physics-informed dynamic mode decomposition (piDMD). CoRR (2021)
Henning Lange, Steven L. Brunton, J. Nathan Kutz
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. J. Mach. Learn. Res. 22 (2021)
Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz
Deep Learning of Conjugate Mappings. CoRR (2021)
Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé
Deeptime: a Python library for machine learning dynamical models from time series data. CoRR (2021)
Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data. CoRR (2021)
Henning Lange, J. Nathan Kutz
FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics. CoRR (2021)
Kadierdan Kaheman, J. Nathan Kutz, Steven L. Brunton
SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics. CoRR (2020)
Brian de Silva, Kathleen P. Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton
PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data. J. Open Source Softw. 5 (49) (2020)
Emily Clark, Angelie Vincent, J. Nathan Kutz, Steven L. Brunton
Bracketing brackets with bras and kets. CoRR (2020)
N. Benjamin Erichson, Peng Zheng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
Sparse Principal Component Analysis via Variable Projection. SIAM J. Appl. Math. 80 (2) (2020)
Daniel Dylewsky, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
Principal Component Trajectories (PCT): Nonlinear dynamics as a superposition of time-delayed periodic orbits. CoRR (2020)
Brian M. de Silva, Jared Callaham, Jonathan Jonker, Nicholas Goebel, Jennifer Klemisch, Darren McDonald, Nathan Hicks, J. Nathan Kutz, Steven L. Brunton, Aleksandr Y. Aravkin
Physics-informed machine learning for sensor fault detection with flight test data. CoRR (2020)
Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz
Discovery of Physics From Data: Universal Laws and Discrepancies. Frontiers Artif. Intell. 3 (2020)
Kathleen P. Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz
A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data. IEEE Access 8 (2020)
Mason Kamb, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
Time-Delay Observables for Koopman: Theory and Applications. SIAM J. Appl. Dyn. Syst. 19 (2) (2020)
Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz
Dynamic mode decomposition for forecasting and analysis of power grid load data. CoRR (2020)
Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz
Multiresolution Convolutional Autoencoders. CoRR (2020)

J. Nathan Kutz.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

J. Nathan Kutz