Stability of Pulses in Nonlinear Optical Fibers Using Phase-Sensitive Amplifiers. - J. Nathan Kutz; William L. Kath (SIAM J. Appl. Math., 1996)

Dynamics and Bifurcations of a Planar Map Modeling Dispersion Managed Breathers. - J. Nathan Kutz; Philip Holmes (SIAM J. Appl. Math., 1999)

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)

Dynamics of the Optical Parametric Oscillator Near Resonance Detuning. - Sarah E. Hewitt; J. Nathan Kutz (SIAM J. Appl. Dyn. Syst., 2005)

Computing spectra of linear operators using the Floquet-Fourier-Hill method. - Bernard Deconinck; J. Nathan Kutz (J. Comput. Phys., 2006)

Mode-Locked Soliton Lasers. - J. Nathan Kutz (SIAM Rev., 2006)

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)

Averaged models for passive mode-locking using nonlinear mode-coupling. - Joshua L. Proctor; J. Nathan Kutz (Math. Comput. Simul., 2007)

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)

Neural Activity Measures and Their Dynamics. - Eli Shlizerman; Konrad Schroder; J. Nathan Kutz (SIAM J. Appl. Math., 2012)

Theoretical studies of frequency domain mode-locked fiber lasers. - Feng Li 029; J. Nathan Kutz; Alexander Ping-Kong Wai (ICAIT, 2013)

Optimal Sensor Placement and Enhanced Sparsity for Classification. - Bingni W. Brunton; Steven L. Brunton; Joshua L. Proctor; J. Nathan Kutz (CoRR, 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)

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)

Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video. - Jacob Grosek; J. Nathan Kutz (CoRR, 2014)

Selecting a Small Set of Optimal Gestures from an Extensive Lexicon. - Jacob Grosek; J. Nathan Kutz (CoRR, 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)

Identifying critical regions for spike propagation in axon segments. - Pedro D. Maia; J. Nathan Kutz (J. Comput. Neurosci., 2014)

Compromised axonal functionality after neurodegeneration, concussion and/or traumatic brain injury. - Pedro D. Maia; J. Nathan Kutz (J. Comput. Neurosci., 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)

A Reaction-Diffusion Model of Cholinergic Retinal Waves. - Benjamin Lansdell; Kevin Ford; J. Nathan Kutz (PLoS Comput. Biol., 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)

Multi-resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking. - J. Nathan Kutz; Xing Fu; Steven L. Brunton; N. Benjamin Erichson (ICCV Workshops, 2015)

Compressed Dynamic Mode Decomposition for Real-Time Object Detection. - N. Benjamin Erichson; Steven L. Brunton; J. Nathan Kutz (CoRR, 2015)

Dynamic mode decomposition - data-driven modeling of complex systems. - J. Nathan Kutz; Steven L. Brunton; Bingni W. Brunton; Joshua L. Proctor (SIAM, 2016)

Randomized Matrix Decompositions using R. - N. Benjamin Erichson; Sergey Voronin; Steven L. Brunton; J. Nathan Kutz (CoRR, 2016)

Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries. - Bethany Lusch; Eric C. Chi; J. Nathan Kutz (CoRR, 2016)

Streaming GPU Singular Value and Dynamic Mode Decompositions. - Seth D. Pendergrass; J. Nathan Kutz; Steven L. Brunton (CoRR, 2016)

Classification of Spatiotemporal Data via Asynchronous Sparse Sampling: Application to Flow around a Cylinder. - Ido Bright; Guang Lin 001; J. Nathan Kutz (Multiscale Model. Simul., 2016)

Multiresolution Dynamic Mode Decomposition. - J. Nathan Kutz; Xing Fu; Steven L. Brunton (SIAM J. Appl. Dyn. Syst., 2016)

Dynamic Mode Decomposition with Control. - Joshua L. Proctor; Steven L. Brunton; J. Nathan Kutz (SIAM J. Appl. Dyn. Syst., 2016)

Sparse Sensor Placement Optimization for Classification. - Bingni W. Brunton; Steven L. Brunton; Joshua L. Proctor; J. Nathan Kutz (SIAM J. Appl. Math., 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)

Machine learning and air quality modeling. - Christoph A. Keller; Mathew J. Evans; J. Nathan Kutz; Steven Pawson (IEEE BigData, 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 (CAMSAP, 2017)

Compressed Singular Value Decomposition for Image and Video Processing. - N. Benjamin Erichson; Steven L. Brunton; J. Nathan Kutz (ICCV Workshops, 2017)

Dynamic Mode Decomposition for Background Modeling. - Seth D. Pendergrass; Steven L. Brunton; J. Nathan Kutz; N. Benjamin Erichson; Travis Askham (ICCV Workshops, 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)

Randomized Dynamic Mode Decomposition. - N. Benjamin Erichson; Steven L. Brunton; J. Nathan Kutz (CoRR, 2017)

Data-Driven Sparse Sensor Placement. - Krithika Manohar; Bingni W. Brunton; J. Nathan Kutz; Steven L. Brunton (CoRR, 2017)

Dynamic mode decomposition for compressive system identification. - Zhe Bai; Eurika Kaiser; Joshua L. Proctor; 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)

Deep learning for universal linear embeddings of nonlinear dynamics. - Bethany Lusch; J. Nathan Kutz; Steven L. Brunton (CoRR, 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)

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)

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)

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)

Nonlinear Model Order Reduction via Dynamic Mode Decomposition. - Alessandro Alla; J. Nathan Kutz (SIAM J. Sci. Comput., 2017)

Discovering Conservation Laws from Data for Control. - Eurika Kaiser; J. Nathan Kutz; Steven L. Brunton (CDC, 2018)

A moth brain learns to read MNIST. - Charles B. Delahunt; J. Nathan Kutz (ICLR (Workshop), 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)

Diffusion Maps meet Nyström. - N. Benjamin Erichson; Lionel Mathelin; Steven L. Brunton; J. Nathan Kutz (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)

Sparse Relaxed Regularized Regression: SR3. - Peng Zheng 002; Travis Askham; Steven L. Brunton; J. Nathan Kutz; Aleksandr Y. Aravkin (CoRR, 2018)

Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data. - Charles B. Delahunt; J. Nathan Kutz (CoRR, 2018)

Optimal Sensor and Actuator Placement using Balanced Model Reduction. - Krithika Manohar; J. Nathan Kutz; Steven L. Brunton (CoRR, 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)

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)

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)

Randomized nonnegative matrix factorization. - N. Benjamin Erichson; Ariana Mendible; Sophie Wihlborn; J. Nathan Kutz (Pattern Recognit. Lett., 2018)

Variable Projection Methods for an Optimized Dynamic Mode Decomposition. - Travis Askham; J. Nathan Kutz (SIAM J. Appl. Dyn. Syst., 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)

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)

Shape Constrained Tensor Decompositions. - Bethany Lusch; Eric C. Chi; J. Nathan Kutz (DSAA, 2019)

A Unified Framework for Sparse Relaxed Regularized Regression: SR3. - Peng Zheng 002; Travis Askham; Steven L. Brunton; J. Nathan Kutz; Aleksandr Y. Aravkin (IEEE Access, 2019)

Randomized model order reduction. - Alessandro Alla; J. Nathan Kutz (Adv. Comput. Math., 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)

Complex Algorithms for Data-Driven Model Learning in Science and Engineering. - Francisco Javier Montáns; Francisco Chinesta; Rafael Gómez-Bombarelli; J. Nathan Kutz (Complex., 2019)

Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy. - Charles B. Delahunt; Courosh Mehanian; 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)

Data-driven multiscale decompositions for forecasting and model discovery. - Daniel Dylewsky; Molei Tao; 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)

Discovery of Physics from Data: Universal Laws and Discrepancy Models. - Brian de Silva; David M. Higdon; 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)

Deep Learning Models for Global Coordinate Transformations that Linearize PDEs. - Craig Gin; Bethany Lusch; Steven L. Brunton; J. Nathan Kutz (CoRR, 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)

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)

Smoothing and parameter estimation by soft-adherence to governing equations. - Samuel H. Rudy; Steven L. Brunton; J. Nathan Kutz (J. Comput. Phys., 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)

Compressed dynamic mode decomposition for background modeling. - N. Benjamin Erichson; Steven L. Brunton; J. Nathan Kutz (J. Real Time Image Process., 2019)

Optimized Sampling for Multiscale Dynamics. - Krithika Manohar; Eurika Kaiser; Steven L. Brunton; J. Nathan Kutz (Multiscale Model. Simul., 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)

Putting a bug in ML: The moth olfactory network learns to read MNIST. - Charles B. Delahunt; J. Nathan Kutz (Neural Networks, 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)

Randomized Dynamic Mode Decomposition. - N. Benjamin Erichson; Lionel Mathelin; J. Nathan Kutz; Steven L. Brunton (SIAM J. Appl. Dyn. Syst., 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)

Numerical Differentiation of Noisy Data: A Unifying Multi-Objective Optimization Framework. - Floris van Breugel; J. Nathan Kutz; Bingni W. Brunton (IEEE Access, 2020)

A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data. - Kathleen P. Champion; Peng Zheng 002; Aleksandr Y. Aravkin; Steven L. Brunton; J. Nathan Kutz (IEEE Access, 2020)

SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics. - Kadierdan Kaheman; J. Nathan Kutz; Steven L. Brunton (CoRR, 2020)

SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems. - Daniel E. Shea; Steven L. Brunton; 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)

Sparse Identification of Slow Timescale Dynamics. - Jason J. Bramburger; Daniel Dylewsky; J. Nathan Kutz (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)

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers. - Yuying Liu; 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)

Bracketing brackets with bras and kets. - Emily Clark; Angelie Vincent; J. Nathan Kutz; Steven L. Brunton (CoRR, 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)

Nonlinear Control in the Nematode C. elegans. - Megan Morrison; Charles Fieseler; J. Nathan Kutz (Frontiers Comput. Neurosci., 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)

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)

Randomized CP tensor decomposition. - N. Benjamin Erichson; Krithika Manohar; Steven L. Brunton; J. Nathan Kutz (Mach. Learn. Sci. Technol., 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)

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)

Time-Delay Observables for Koopman: Theory and Applications. - Mason Kamb; Eurika Kaiser; Steven L. Brunton; J. Nathan Kutz (SIAM J. Appl. Dyn. Syst., 2020)

Sparse Principal Component Analysis via Variable Projection. - N. Benjamin Erichson; Peng Zheng 002; Krithika Manohar; Steven L. Brunton; J. Nathan Kutz; Aleksandr Y. Aravkin (SIAM J. Appl. Math., 2020)

SINDy with Control: A Tutorial. - Urban Fasel; Eurika Kaiser; J. Nathan Kutz; Bingni W. Brunton; Steven L. Brunton (CDC, 2021)

Data-Driven Stabilization of Periodic Orbits. - Jason J. Bramburger; J. Nathan Kutz; Steven L. Brunton (IEEE Access, 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)

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)

Deep Learning of Conjugate Mappings. - Jason J. Bramburger; Steven L. Brunton; 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 Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties. - Alex Mallen; 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)

FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics. - Henning Lange; J. Nathan Kutz (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)

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)

Physics-informed dynamic mode decomposition (piDMD). - Peter J. Baddoo; Benjamin Herrmann; Beverley J. McKeon; J. Nathan Kutz; Steven L. Brunton (CoRR, 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)

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)

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 (Zenodo, 2021)

Data-driven discovery of Koopman eigenfunctions for control. - Eurika Kaiser; J. Nathan Kutz; Steven L. Brunton (Mach. Learn. Sci. Technol., 2021)

A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes. - Charles B. Delahunt; J. Nathan Kutz (IEEE Access, 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)

Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders. - Joseph Bakarji; Kathleen P. Champion; 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; et al. (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)

Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data. - Alex Mallen; Christoph A. Keller; J. Nathan Kutz (CoRR, 2022)

Spatiotemporal k-means. - Olga Dorabiala; Jennifer Webster; J. Nathan Kutz; Aleksandr Y. Aravkin (CoRR, 2022)

Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. - L. Mars Gao; J. Nathan Kutz (CoRR, 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)

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)

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)

Deeptime: a Python library for machine learning dynamical models from time series data. - Moritz Hoffmann 002; Martin Scherer; Tim Hempel; Andreas Mardt; Brian de Silva; Brooke E. Husic; Stefan Klus; Hao Wu 035; J. Nathan Kutz; Steven L. Brunton; Frank Noé (Mach. Learn. Sci. Technol., 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)

Dimensionally consistent learning with Buckingham Pi. - Joseph Bakarji; Jared Callaham; Steven L. Brunton; J. Nathan Kutz (Nat. Comput. Sci., 2022)

Robust trimmed k-means. - Olga Dorabiala; J. Nathan Kutz; Aleksandr Y. Aravkin (Pattern Recognit. Lett., 2022)

Robust and Scalable Methods for the Dynamic Mode Decomposition. - Travis Askham; Peng Zheng 002; Aleksandr Y. Aravkin; J. Nathan Kutz (SIAM J. Appl. Dyn. Syst., 2022)

Modern Koopman Theory for Dynamical Systems. - Steven L. Brunton; Marko Budisic; Eurika Kaiser; J. Nathan Kutz (SIAM Rev., 2022)

Optimal Sensor and Actuator Selection Using Balanced Model Reduction. - Krithika Manohar; J. Nathan Kutz; Steven L. Brunton (IEEE Trans. Autom. Control., 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., 2022)

Model predictive control for robust quantum state preparation. - Andy Goldschmidt; Jonathan L. Dubois; Steven L. Brunton; J. Nathan Kutz (Quantum, 2022)

Observability-Based Energy Efficient Path Planning with Background Flow via Deep Reinforcement Learning. - Jiazhong Mei; J. Nathan Kutz; Steven L. Brunton (CDC, 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 003; Jonathan P. How (ICRA, 2023)

Extremum Seeking Control of Quantum Gates. - Erfan Abbasgholinejad; Haoqin Deng; John King Gamble; J. Nathan Kutz; Erik Nielsen; Neal C. Pisenti; Ningzhi Xie (QCE, 2023)

Non-Stationary Dynamic Mode Decomposition. - John Ferré; Ariel Rokem; Elizabeth A. Buffalo; J. Nathan Kutz; Adrienne Fairhall (IEEE Access, 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)

Machine Learning for Partial Differential Equations. - Steven L. Brunton; J. Nathan Kutz (CoRR, 2023)

Data-Induced Interactions of Sparse Sensors. - Andrei A. Klishin; J. Nathan Kutz; Krithika Manohar (CoRR, 2023)

Nonlinear parametric models of viscoelastic fluid flows. - Cassio M. Oishi; Alan A. Kaptanoglu; J. Nathan Kutz; Steven L. Brunton (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)

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)

Multi-fidelity reduced-order surrogate modeling. - Paolo Conti; Mengwu Guo; Andrea Manzoni; Attilio Frangi; Steven L. Brunton; J. Nathan Kutz (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)

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)

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)

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)

Multiresolution convolutional autoencoders. - Yuying Liu; Colin Ponce; Steven L. Brunton; J. Nathan Kutz (J. Comput. Phys., 2023)

Walking Droplets as a Damped-Driven System. - Aminur Rahman; J. Nathan Kutz (SIAM J. Appl. Dyn. Syst., 2023)

Transitions between peace and systemic war as bifurcations in a signed network dynamical system. - Megan Morrison; J. Nathan Kutz; Michael Gabbay 002 (Netw. Sci., 2023)

The Adaptive Spectral Koopman Method for Dynamical Systems. - Bian Li; Yi-An Ma; J. Nathan Kutz; Xiu Yang (SIAM J. Appl. Dyn. Syst., 2023)

Data-Driven Discovery of Governing Equations for Coarse-Grained Heterogeneous Network Dynamics. - Katherine Owens; J. Nathan Kutz (SIAM J. Appl. Dyn. Syst., 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., 2023)

Single-Pixel Imaging Of Dynamic Flows Using Neural Ode Regularization. - Aleksei Sholokhov; Joshua Rapp; Saleh Nabi; Steven L. Brunton; J. Nathan Kutz; Hassan Mansour (ICASSP, 2024)

Ensemble Principal Component Analysis. - Olga Dorabiala; Aleksandr Y. Aravkin; J. Nathan Kutz (IEEE Access, 2024)

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 (IEEE Access, 2024)

Learning Nonlinear Dynamics Using Kalman Smoothing. - Jacob Stevens-Haas; Yash Bhangale; J. Nathan Kutz; Aleksandr Y. Aravkin (IEEE Access, 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 002 (AI Mag., 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)

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)

Motif distribution and function of sparse deep neural networks. - Olivia T. Zahn; Thomas L. Daniel; 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)

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)

Statistical Mechanics of Dynamical System Identification. - Andrei A. Klishin; Joseph Bakarji; J. Nathan Kutz; Krithika Manohar (CoRR, 2024)

SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning. - Nicholas Zolman; Urban Fasel; J. Nathan Kutz; Steven L. Brunton (CoRR, 2024)

Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data. - Meghana Velegar; Christoph Keller; J. Nathan Kutz (CoRR, 2024)

Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics. - J. Nathan Kutz; Maryam Reza; Farbod Faraji; Aaron Knoll (CoRR, 2024)

VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. - Paolo Conti; Jonas Kneifl; Andrea Manzoni; Attilio Frangi; Jörg Fehr; Steven L. Brunton; J. Nathan Kutz (CoRR, 2024)

Automating the Practice of Science - Opportunities, Challenges, and Implications. - Sebastian Musslick; Laura Bartlett; Suyog H. Chandramouli; Marina Dubova; Fernand Gobet; Thomas L. Griffiths 001; Jessica Hullman; Ross D. King; J. Nathan Kutz; Christopher G. Lucas; Suhas Mahesh; Franco Pestilli; et al. (CoRR, 2024)

Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors. - Stefano Riva; Carolina Introini; Antonio Cammi; J. Nathan Kutz (CoRR, 2024)

AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues. - Oliver Bensch; Leonie Bensch; Tommy Nilsson; Florian Saling; Bernd Bewer; Sophie Jentzsch; Tobias Hecking; J. Nathan Kutz (CoRR, 2024)

Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems. - Doris Voina; Steven L. Brunton; J. Nathan Kutz (CoRR, 2024)

Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight. - Oliver Bensch; Leonie Bensch; Tommy Nilsson; Florian Saling; Wafa Sadri; Carsten Hartmann; Tobias Hecking; J. Nathan Kutz (CoRR, 2024)

Reservoir computing for system identification and predictive control with limited data. - Jan P. Williams; J. Nathan Kutz; Krithika Manohar (CoRR, 2024)

Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics. - Aleksei Sholokhov; Saleh Nabi; Joshua Rapp; Steven L. Brunton; J. Nathan Kutz; Petros T. Boufounos; Hassan Mansour (IEEE Trans. Computational Imaging, 2024)

dynamicslab/pykoopman: v1.0.9. - Shaowu Pan; Eurika Kaiser; Brian M. de Silva; J. Nathan Kutz; Steven L. Brunton (Zenodo, 2024)

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 (Zenodo, 2024)

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 (Zenodo, 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., 2024)

Solving Nonlinear Ordinary Differential Equations Using the Invariant Manifolds and Koopman Eigenfunctions. - Megan Morrison; J. Nathan Kutz (SIAM J. Appl. Dyn. Syst., 2024)

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 (J. Open Source Softw., 2024)

Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation. - Jiazhong Mei; Steven L. Brunton; J. Nathan Kutz (Sensors, 2024)

Promising directions of machine learning for partial differential equations. - Steven L. Brunton; J. Nathan Kutz (Nat. Comput. Sci., 2024)

Physics-Enforced Reservoir Computing for Forecasting Spatiotemporal Systems. - Dima Tretiak; Anastasia Bizyaeva; J. Nathan Kutz; Steven L. Brunton (L4DC, 2025)

Machine Learning for Sparse Nonlinear Modeling and Control. - Steven L. Brunton; Nicholas Zolman; J. Nathan Kutz; Urban Fasel (Annu. Rev. Control. Robotics Auton. Syst., 2025)

Shallow recurrent decoder for reduced order modeling of E × B plasma dynamics. - Farbod Faraji; Maryam Reza; J. Nathan Kutz (Mach. Learn. Sci. Technol., 2025)

Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks. - Mars Liyao Gao; Jan P. Williams; J. Nathan Kutz (CoRR, 2025)

Inverse Design with Dynamic Mode Decomposition. - Yunpeng Zhu; Liangliang Cheng; Anping Jing; Hanyu Huo; Ziqiang Lang; Bo Zhang; J. Nathan Kutz (CoRR, 2025)

Reduced Order Modeling with Shallow Recurrent Decoder Networks. - Matteo Tomasetto; Jan P. Williams; Francesco Braghin; Andrea Manzoni; J. Nathan Kutz (CoRR, 2025)

Coarse graining and reduced order models for plume ejection dynamics. - Ike Griss Salas; Megan R. Ebers; Jake Stevens-Haas; J. Nathan Kutz (CoRR, 2025)

Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks. - Stefano Riva; Carolina Introini; J. Nathan Kutz; Antonio Cammi (CoRR, 2025)

From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility. - Carolina Introini; Stefano Riva; J. Nathan Kutz; Antonio Cammi (CoRR, 2025)

Mesh-free sparse identification of nonlinear dynamics. - Mars Liyao Gao; J. Nathan Kutz; Bernat Font (CoRR, 2025)

T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders. - Alexey Yermakov; David Zoro; Mars Liyao Gao; J. Nathan Kutz (CoRR, 2025)

Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures. - Sara M. Ichinaga; Steven L. Brunton; Aleksandr Y. Aravkin; J. Nathan Kutz (CoRR, 2025)

PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery. - David Ye; Jan P. Williams; Mars Liyao Gao; Stefano Riva; Matteo Tomasetto; David Zoro; J. Nathan Kutz (CoRR, 2025)

CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics. - Romulo B. da Silva; Diego Passos; Cassio M. Oishi; J. Nathan Kutz (CoRR, 2025)