J. Nathan Kutz

According to our database, J. Nathan Kutz authored at least 174 papers between 1996 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects.

Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., March, 2024

SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning.

Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Brunton

CoRR, 2024

Statistical Mechanics of Dynamical System Identification.

Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz, Krithika Manohar

CoRR, 2024

Data-driven local operator finding for reduced-order modelling of plasma systems: II. Application to parametric dynamics.

Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz

CoRR, 2024

Data-driven local operator finding for reduced-order modelling of plasma systems: I. Concept and verifications.

Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz

CoRR, 2024

Motif distribution and function of sparse deep neural networks.

Olivia T. Zahn, Thomas L. Daniel, J. Nathan Kutz

CoRR, 2024

Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks.

Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz

CoRR, 2024

PyDMD: A Python package for robust dynamic mode decomposition.

Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, J. Nathan Kutz

CoRR, 2024

AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data-driven control.

J. Nathan Kutz, Steven L. Brunton, Krithika Manohar, Hod Lipson, Na Li

AI Mag., 2024

Ensemble Principal Component Analysis.

Olga Dorabiala, Aleksandr Y. Aravkin, J. Nathan Kutz

IEEE Access, 2024

2023

Deep learning based object tracking in walking droplet and granular intruder experiments.

Erdi Kara, George Zhang, Joseph J. Williams, Gonzalo Ferrandez-Quinto, Leviticus J. Rhoden, Maximilian Kim, J. Nathan Kutz, Aminur Rahman

J. Real Time Image Process., October, 2023

Data-Driven Discovery of Governing Equations for Coarse-Grained Heterogeneous Network Dynamics.

Katherine Owens, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., September, 2023

The Adaptive Spectral Koopman Method for Dynamical Systems.

Bian Li, Yi-An Ma, J. Nathan Kutz, Xiu Yang

SIAM J. Appl. Dyn. Syst., September, 2023

Transitions between peace and systemic war as bifurcations in a signed network dynamical system.

Megan Morrison, J. Nathan Kutz, Michael Gabbay

Netw. Sci., September, 2023

Walking Droplets as a Damped-Driven System.

Aminur Rahman, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., June, 2023

Multiresolution convolutional autoencoders.

Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz

J. Comput. Phys., February, 2023

Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data.

Shaowu Pan, Steven L. Brunton, J. Nathan Kutz

J. Mach. Learn. Res., 2023

Attention for Causal Relationship Discovery from Biological Neural Dynamics.

Ziyu Lu, Anika Tabassum, Shruti R. Kulkarni, Lu Mi, J. Nathan Kutz, Eric Shea-Brown, Seung-Hwan Lim

CoRR, 2023

A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning.

Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton

CoRR, 2023

HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations.

Mozes Jacobs, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz, Ryan V. Raut

CoRR, 2023

Multi-fidelity reduced-order surrogate modeling.

Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz

CoRR, 2023

Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: II. dynamics forecasting.

Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz

CoRR, 2023

Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: I. Extraction of spatiotemporally coherent patterns.

Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz

CoRR, 2023

Nonlinear parametric models of viscoelastic fluid flows.

Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz, Steven L. Brunton

CoRR, 2023

Data-Induced Interactions of Sparse Sensors.

Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar

CoRR, 2023

Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction.

Megan R. Ebers, Jan P. Williams, Katherine M. Steele, J. Nathan Kutz

CoRR, 2023

PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator.

Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton

CoRR, 2023

Machine Learning for Partial Differential Equations.

Steven L. Brunton, J. Nathan Kutz

CoRR, 2023

Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery.

L. Mars Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz

CoRR, 2023

Non-Stationary Dynamic Mode Decomposition.

John Ferré, Ariel Rokem, Elizabeth A. Buffalo, J. Nathan Kutz, Adrienne Fairhall

IEEE Access, 2023

Extremum Seeking Control of Quantum Gates.

Erfan Abbasgholinejad, Haoqin Deng, John King Gamble, J. Nathan Kutz, Erik Nielsen, Neal C. Pisenti, Ningzhi Xie

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC.

Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

Observability-Based Energy Efficient Path Planning with Background Flow via Deep Reinforcement Learning.

Jiazhong Mei, J. Nathan Kutz, Steven L. Brunton

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022

Model predictive control for robust quantum state preparation.

Andy Goldschmidt, Jonathan L. Dubois, Steven L. Brunton, J. Nathan Kutz

Quantum, September, 2022

Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight.

Olivia Zahn, Jorge Bustamante, Callin Switzer, Thomas L. Daniel, J. Nathan Kutz

PLoS Comput. Biol., September, 2022

Optimal Sensor and Actuator Selection Using Balanced Model Reduction.

Krithika Manohar, J. Nathan Kutz, Steven L. Brunton

IEEE Trans. Autom. Control., 2022

Modern Koopman Theory for Dynamical Systems.

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

SIAM Rev., 2022

Robust and Scalable Methods for the Dynamic Mode Decomposition.

Travis Askham, Peng Zheng, Aleksandr Y. Aravkin, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2022

Robust trimmed k-means.

Olga Dorabiala, J. Nathan Kutz, Aleksandr Y. Aravkin

Pattern Recognit. Lett., 2022

Dimensionally consistent learning with Buckingham Pi.

Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz

Nat. Comput. Sci., 2022

Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data.

Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz

Mach. Learn. Sci. Technol., 2022

Deeptime: a Python library for machine learning dynamical models from time series data.

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

Mach. Learn. Sci. Technol., 2022

PySINDy: A comprehensive Python package for robust sparse system identification.

Alan A. Kaptanoglu, Brian de Silva, Urban Fasel, Kadierdan Kaheman, Andy Goldschmidt, Jared Callaham, Charles B. Delahunt, Zachary Nicolaou, Kathleen P. Champion, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton

J. Open Source Softw., 2022

PyNumDiff: A Python package for numerical differentiation of noisy time-series data.

Floris van Breugel, Yuying Liu, Bingni W. Brunton, J. Nathan Kutz

J. Open Source Softw., 2022

Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning.

Nina de Lacy, Michael J. Ramshaw, J. Nathan Kutz

Frontiers Artif. Intell., 2022

Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants.

L. Mars Gao, J. Nathan Kutz

CoRR, 2022

Spatiotemporal k-means.

Olga Dorabiala, Jennifer Webster, J. Nathan Kutz, Aleksandr Y. Aravkin

CoRR, 2022

Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data.

Alex Mallen, Christoph A. Keller, J. Nathan Kutz

CoRR, 2022

The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control.

Kadierdan Kaheman, Urban Fasel, Jason J. Bramburger, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton

CoRR, 2022

Machine Learning in Heterogeneous Porous Materials.

Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna C. 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

CoRR, 2022

Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders.

Joseph Bakarji, Kathleen P. Champion, J. Nathan Kutz, Steven L. Brunton

CoRR, 2022

Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems.

Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz

IEEE Access, 2022

A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes.

Charles B. Delahunt, J. Nathan Kutz

IEEE Access, 2022

2021

Data-driven discovery of Koopman eigenfunctions for control.

Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton

Mach. Learn. Sci. Technol., September, 2021

Nonlinear Control of Networked Dynamical Systems.

Megan Morrison, J. Nathan Kutz

IEEE Trans. Netw. Sci. Eng., 2021

PySensors: A Python package for sparse sensor placement.

Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton

J. Open Source Softw., 2021

From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction.

Henning Lange, Steven L. Brunton, J. Nathan Kutz

J. Mach. Learn. Res., 2021

Physics-informed dynamic mode decomposition (piDMD).

Peter J. Baddoo, Benjamin Herrmann, Beverley J. McKeon, J. Nathan Kutz, Steven L. Brunton

CoRR, 2021

Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.

Urban Fasel, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton

CoRR, 2021

PySINDy: A comprehensive Python package for robust sparse system identification.

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

CoRR, 2021

FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics.

Henning Lange, J. Nathan Kutz

CoRR, 2021

Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification.

Diya Sashidhar, J. Nathan Kutz

CoRR, 2021

Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties.

Alex Mallen, Henning Lange, J. Nathan Kutz

CoRR, 2021

Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations.

Manu Kalia, Steven L. Brunton, Hil G. E. Meijer, Christoph Brune, J. Nathan Kutz

CoRR, 2021

Deep Learning of Conjugate Mappings.

Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz

CoRR, 2021

DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems.

Craig R. Gin, Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz

CoRR, 2021

Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data.

Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz

IEEE Access, 2021

Data-Driven Stabilization of Periodic Orbits.

Jason J. Bramburger, J. Nathan Kutz, Steven L. Brunton

IEEE Access, 2021

SINDy with Control: A Tutorial.

Urban Fasel, Eurika Kaiser, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Sparse Principal Component Analysis via Variable Projection.

N. Benjamin Erichson, Peng Zheng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin

SIAM J. Appl. Math., 2020

Time-Delay Observables for Koopman: Theory and Applications.

Mason Kamb, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2020

Centering Data Improves the Dynamic Mode Decomposition.

Seth M. Hirsh, Kameron Decker Harris, J. Nathan Kutz, Bingni W. Brunton

SIAM J. Appl. Dyn. Syst., 2020

Deep reinforcement learning for optical systems: A case study of mode-locked lasers.

Chang Sun, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz

Mach. Learn. Sci. Technol., 2020

Randomized CP tensor decomposition.

N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz

Mach. Learn. Sci. Technol., 2020

PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data.

Brian de Silva, Kathleen P. Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton

J. Open Source Softw., 2020

Discovery of Physics From Data: Universal Laws and Discrepancies.

Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz

Frontiers Artif. Intell., 2020

Nonlinear Control in the Nematode C. elegans.

Megan Morrison, Charles Fieseler, J. Nathan Kutz

Frontiers Comput. Neurosci., 2020

Dynamic mode decomposition for forecasting and analysis of power grid load data.

Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz

CoRR, 2020

Bracketing brackets with bras and kets.

Emily Clark, Angelie Vincent, J. Nathan Kutz, Steven L. Brunton

CoRR, 2020

Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning.

Steven L. Brunton, J. Nathan Kutz, Krithika Manohar, Aleksandr Y. Aravkin, Kristi Morgansen, Jennifer Klemisch, Nicholas Goebel, James Buttrick, Jeffrey Poskin, Agnes Blom-Schieber, Thomas A. Hogan, Darren McDonald

CoRR, 2020

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers.

Yuying Liu, J. Nathan Kutz, Steven L. Brunton

CoRR, 2020

Physics-informed machine learning for sensor fault detection with flight test data.

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

CoRR, 2020

Sparse Identification of Slow Timescale Dynamics.

Jason J. Bramburger, Daniel Dylewsky, J. Nathan Kutz

CoRR, 2020

Principal Component Trajectories (PCT): Nonlinear dynamics as a superposition of time-delayed periodic orbits.

Daniel Dylewsky, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz

CoRR, 2020

SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems.

Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz

CoRR, 2020

SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics.

Kadierdan Kaheman, J. Nathan Kutz, Steven L. Brunton

CoRR, 2020

A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data.

Kathleen P. Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz

IEEE Access, 2020

Numerical Differentiation of Noisy Data: A Unifying Multi-Objective Optimization Framework.

Floris van Breugel, J. Nathan Kutz, Bingni W. Brunton

IEEE Access, 2020

2019

Data-Driven Identification of Parametric Partial Differential Equations.

Samuel H. Rudy, Alessandro Alla, Steven L. Brunton, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2019

Randomized Dynamic Mode Decomposition.

N. Benjamin Erichson, Lionel Mathelin, J. Nathan Kutz, Steven L. Brunton

SIAM J. Appl. Dyn. Syst., 2019

Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings.

Kathleen P. Champion, Steven L. Brunton, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2019

Putting a bug in ML: The moth olfactory network learns to read MNIST.

Charles B. Delahunt, J. Nathan Kutz

Neural Networks, 2019

Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates.

Nina de Lacy, Elizabeth McCauley, J. Nathan Kutz, Vince D. Calhoun

NeuroImage, 2019

Optimized Sampling for Multiscale Dynamics.

Krithika Manohar, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz

Multiscale Model. Simul., 2019

Compressed dynamic mode decomposition for background modeling.

N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz

J. Real Time Image Process., 2019

Deep learning of dynamics and signal-noise decomposition with time-stepping constraints.

Samuel H. Rudy, J. Nathan Kutz, Steven L. Brunton

J. Comput. Phys., 2019

Smoothing and parameter estimation by soft-adherence to governing equations.

Samuel H. Rudy, Steven L. Brunton, J. Nathan Kutz

J. Comput. Phys., 2019

Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries.

Pedro D. Maia, Ashish Raj, J. Nathan Kutz

J. Comput. Neurosci., 2019

Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition.

James M. Kunert-Graf, Kristian M. Eschenburg, David J. Galas, J. Nathan Kutz, Swati D. Rane, Bingni W. Brunton

Frontiers Comput. Neurosci., 2019

Deep Learning Models for Global Coordinate Transformations that Linearize PDEs.

Craig Gin, Bethany Lusch, Steven L. Brunton, J. Nathan Kutz

CoRR, 2019

Learning Discrepancy Models From Experimental Data.

Kadierdan Kaheman, Eurika Kaiser, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton

CoRR, 2019

Discovery of Physics from Data: Universal Laws and Discrepancy Models.

Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz

CoRR, 2019

Deep Model Predictive Control with Online Learning for Complex Physical Systems.

Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz

CoRR, 2019

Data-driven multiscale decompositions for forecasting and model discovery.

Daniel Dylewsky, Molei Tao, J. Nathan Kutz

CoRR, 2019

Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data.

N. Benjamin Erichson, Lionel Mathelin, Zhewei Yao, Steven L. Brunton, Michael W. Mahoney, J. Nathan Kutz

CoRR, 2019

Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy.

Charles B. Delahunt, Courosh Mehanian, J. Nathan Kutz

CoRR, 2019

Complex Algorithms for Data-Driven Model Learning in Science and Engineering.

Francisco Javier Montáns, Francisco Chinesta, Rafael Gómez-Bombarelli, J. Nathan Kutz

Complex., 2019

Engineering structural robustness in power grid networks susceptible to community desynchronization.

Daniel Dylewsky, Xiu Yang, Alexandre M. Tartakovsky, J. Nathan Kutz

Appl. Netw. Sci., 2019

Randomized model order reduction.

Alessandro Alla, J. Nathan Kutz

Adv. Comput. Math., 2019

A Unified Framework for Sparse Relaxed Regularized Regression: SR3.

Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin

IEEE Access, 2019

Shape Constrained Tensor Decompositions.

Bethany Lusch, Eric C. Chi, J. Nathan Kutz

Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics, 2019

2018

Online Interpolation Point Refinement for Reduced-Order Models using a Genetic Algorithm.

Syuzanna Sargsyan, Steven L. Brunton, J. Nathan Kutz

SIAM J. Sci. Comput., 2018

Generalizing Koopman Theory to Allow for Inputs and Control.

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

SIAM J. Appl. Dyn. Syst., 2018

Variable Projection Methods for an Optimized Dynamic Mode Decomposition.

Travis Askham, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2018

Randomized nonnegative matrix factorization.

N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz

Pattern Recognit. Lett., 2018

Sparsity enabled cluster reduced-order models for control.

Eurika Kaiser, Marek Morzynski, Guillaume Daviller, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton

J. Comput. Phys., 2018

Structural Load Estimation Using Machine Vision and Surface Crack Patterns for Shear-Critical RC Beams and Slabs.

Rouzbeh Davoudi, Gregory R. Miller, J. Nathan Kutz

J. Comput. Civ. Eng., 2018

Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, With Applications to Neural Nets.

Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz

Frontiers Comput. Neurosci., 2018

Optimal Sensor and Actuator Placement using Balanced Model Reduction.

Krithika Manohar, J. Nathan Kutz, Steven L. Brunton

CoRR, 2018

Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data.

Charles B. Delahunt, J. Nathan Kutz

CoRR, 2018

Sparse Relaxed Regularized Regression: SR3.

Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin

CoRR, 2018

Sparse Principal Component Analysis via Variable Projection.

N. Benjamin Erichson, Peng Zeng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin

CoRR, 2018

Diffusion Maps meet Nyström.

N. Benjamin Erichson, Lionel Mathelin, Steven L. Brunton, J. Nathan Kutz

CoRR, 2018

Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems.

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

Complex., 2018

A moth brain learns to read MNIST.

Charles B. Delahunt, J. Nathan Kutz

Proceedings of the 6th International Conference on Learning Representations, 2018

Discovering Conservation Laws from Data for Control.

Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Nonlinear Model Order Reduction via Dynamic Mode Decomposition.

Alessandro Alla, J. Nathan Kutz

SIAM J. Sci. Comput., 2017

Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion.

James M. Kunert, Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz

PLoS Comput. Biol., 2017

Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage.

James M. Kunert, Pedro D. Maia, J. Nathan Kutz

PLoS Comput. Biol., 2017

Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurological diseases.

Pedro D. Maia, J. Nathan Kutz

J. Comput. Neurosci., 2017

Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics.

James M. Kunert-Graf, Eli Shlizerman, Andrew Walker, J. Nathan Kutz

Frontiers Comput. Neurosci., 2017

Deep learning for universal linear embeddings of nonlinear dynamics.

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

CoRR, 2017

Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers.

Thomas Baumeister, Steven L. Brunton, J. Nathan Kutz

CoRR, 2017

Dynamic mode decomposition for compressive system identification.

Zhe Bai, Eurika Kaiser, Joshua L. Proctor, J. Nathan Kutz, Steven L. Brunton

CoRR, 2017

Data-Driven Sparse Sensor Placement.

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

CoRR, 2017

Randomized Dynamic Mode Decomposition.

N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz

CoRR, 2017

Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External Microelectronics.

Megan Morrison, Pedro D. Maia, J. Nathan Kutz

Comput. Math. Methods Medicine, 2017

Dynamic Mode Decomposition for Background Modeling.

Seth D. Pendergrass, Steven L. Brunton, J. Nathan Kutz, N. Benjamin Erichson, Travis Askham

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

Compressed Singular Value Decomposition for Image and Video Processing.

N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

Data-Driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology.

J. Nathan Kutz, Samuel H. Rudy, Alessandro Alla, Steven L. Brunton

Proceedings of the 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017

Machine learning and air quality modeling.

Christoph A. Keller, Mathew J. Evans, J. Nathan Kutz, Steven Pawson

Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016

Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics.

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

IEEE Trans. Mol. Biol. Multi Scale Commun., 2016

Sparse Sensor Placement Optimization for Classification.

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

SIAM J. Appl. Math., 2016

Dynamic Mode Decomposition with Control.

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

SIAM J. Appl. Dyn. Syst., 2016

Multiresolution Dynamic Mode Decomposition.

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

SIAM J. Appl. Dyn. Syst., 2016

Classification of Spatiotemporal Data via Asynchronous Sparse Sampling: Application to Flow around a Cylinder.

Ido Bright, Guang Lin, J. Nathan Kutz

Multiscale Model. Simul., 2016

Streaming GPU Singular Value and Dynamic Mode Decompositions.

Seth D. Pendergrass, J. Nathan Kutz, Steven L. Brunton

CoRR, 2016

Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries.

Bethany Lusch, Eric C. Chi, J. Nathan Kutz

CoRR, 2016

Randomized Matrix Decompositions using R.

N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz

CoRR, 2016

Dynamic mode decomposition - data-driven modeling of complex systems.

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

SIAM, ISBN: 978-1-611-97449-2, 2016

2015

Compressed Dynamic Mode Decomposition for Real-Time Object Detection.

N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz

CoRR, 2015

Multi-resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking.

J. Nathan Kutz, Xing Fu, Steven L. Brunton, N. Benjamin Erichson

Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop, 2015

2014

Compressive Sensing and Low-Rank Libraries for Classification of Bifurcation Regimes in Nonlinear Dynamical Systems.

Steven L. Brunton, Jonathan H. Tu, Ido Bright, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2014

A Reaction-Diffusion Model of Cholinergic Retinal Waves.

Benjamin Lansdell, Kevin Ford, J. Nathan Kutz

PLoS Comput. Biol., 2014

Herpes Simplex Virus-2 Genital Tract Shedding Is Not Predictable over Months or Years in Infected Persons.

Varsha Dhankani, J. Nathan Kutz, Joshua T. Schiffer

PLoS Comput. Biol., 2014

Compromised axonal functionality after neurodegeneration, concussion and/or traumatic brain injury.

Pedro D. Maia, J. Nathan Kutz

J. Comput. Neurosci., 2014

Identifying critical regions for spike propagation in axon segments.

Pedro D. Maia, J. Nathan Kutz

J. Comput. Neurosci., 2014

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe.

Eli Shlizerman, Jeffrey Riffell, J. Nathan Kutz

Frontiers Comput. Neurosci., 2014

Selecting a Small Set of Optimal Gestures from an Extensive Lexicon.

Jacob Grosek, J. Nathan Kutz

CoRR, 2014

Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video.

Jacob Grosek, J. Nathan Kutz

CoRR, 2014

2013

Erratum: Hybrid Reduced-Order Integration with Proper Orthogonal Decomposition and Dynamic Mode Decomposition.

Matthew O. Williams, Peter J. Schmid, J. Nathan Kutz

Multiscale Model. Simul., 2013

Hybrid Reduced-Order Integration with Proper Orthogonal Decomposition and Dynamic Mode Decomposition.

Matthew O. Williams, Peter J. Schmid, J. Nathan Kutz

Multiscale Model. Simul., 2013

Optimal Sensor Placement and Enhanced Sparsity for Classification.

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

CoRR, 2013

Theoretical studies of frequency domain mode-locked fiber lasers.

Feng Li, J. Nathan Kutz, Alexander Ping-Kong Wai

Proceedings of the 6th IEEE International Conference on Advanced Infocomm Technology, 2013

2012

Neural Activity Measures and Their Dynamics.

Eli Shlizerman, Konrad Schroder, J. Nathan Kutz

SIAM J. Appl. Math., 2012

The Low Dimensionality of Time-Periodic Standing Waves in Water of Finite and Infinite Depth.

Matthew O. Williams, Eli Shlizerman, Jon Wilkening, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2012

2007

Averaged models for passive mode-locking using nonlinear mode-coupling.

Joshua L. Proctor, J. Nathan Kutz

Math. Comput. Simul., 2007

SpectrUW: A laboratory for the numerical exploration of spectra of linear operators.

Bernard Deconinck, Firat Kiyak, John D. Carter, J. Nathan Kutz

Math. Comput. Simul., 2007

2006

Mode-Locked Soliton Lasers.

J. Nathan Kutz

SIAM Rev., 2006

Computing spectra of linear operators using the Floquet-Fourier-Hill method.

Bernard Deconinck, J. Nathan Kutz

J. Comput. Phys., 2006

2005

Dynamics of the Optical Parametric Oscillator Near Resonance Detuning.

Sarah E. Hewitt, J. Nathan Kutz

SIAM J. Appl. Dyn. Syst., 2005

2002

Dynamics and Stability of Bose-Einstein Condensates: The Nonlinear Schrödinger Equation with Periodic Potential.

Bernard Deconinck, Bela A. Frigyik, J. Nathan Kutz

J. Nonlinear Sci., 2002

1999

Dynamics and Bifurcations of a Planar Map Modeling Dispersion Managed Breathers.

J. Nathan Kutz, Philip Holmes

SIAM J. Appl. Math., 1999

1996

Stability of Pulses in Nonlinear Optical Fibers Using Phase-Sensitive Amplifiers.

J. Nathan Kutz, William L. Kath

SIAM J. Appl. Math., 1996