J. Nathan Kutz

According to our database, J. Nathan Kutz authored at least 106 papers between 1996 and 2021.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

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

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

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

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

Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data.

Kadierdan Kaheman, Steven L. Brunton, 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 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

Multiresolution Convolutional Autoencoders.

Yuying Liu, Colin Ponce, 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

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

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

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 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 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

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 CP Tensor Decomposition.

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

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, 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