J. Nathan Kutz

251 papers A* 1B 1Misc 2Journal 235Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	An Interpretable Data-Driven Model of the Flight Dynamics of Hawks. CoRR Lydia France, Karl Lapo, J. Nathan Kutz
2026	J	`jnl`	Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics - Rotating Detonation Engines. CoRR Yuxuan Bao, Jan Zajac, Megan Powers, Venkat Raman, J. Nathan Kutz
2026	J	`jnl`	Experimental Modal Analysis for engineering structures via time-delay Dynamic Mode Decomposition with Control. CoRR Yanxin Si, Bayu Jayawardhana, J. Nathan Kutz, Yunpeng Zhu, Liangliang Cheng
2026	J	`jnl`	OpenReservoirComputing: GPU-Accelerated Reservoir Computing in JAX. CoRR Jan P. Williams, Dima Tretiak, Steven L. Brunton, J. Nathan Kutz, Krithika Manohar
2026	J	`jnl`	PySensors 2.0: A Python Package for Sparse Sensor Placement. J. Open Source Softw. Niharika Karnik, Yash Bhangale, Mohammad G. Abdo, Andrei A. Klishin, Joshua J. Cogliati, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton, Krithika Manohar
2026	J	`jnl`	SENDAI: A Hierarchical Sparse-measurement, EfficieNt Data AssImilation Framework. CoRR Xingyue Zhang, Yuxuan Bao, Mars Liyao Gao, J. Nathan Kutz
2026	J	`jnl`	Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics. CoRR M. Lo Verso, Carolina Introini, E. Cervi, L. Savoldi, J. Nathan Kutz, Antonio Cammi
2026	J	`jnl`	Terrain-Adaptive Mobile 3D Printing with Hierarchical Control. CoRR Shuangshan Nors Li, J. Nathan Kutz
2026	J	`jnl`	VENI, VINDy, VICI: A generative reduced-order modeling framework with uncertainty quantification. Neural Networks Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
2025	J	`jnl`	A Taxonomy of Numerical Differentiation Methods. CoRR Pavel Komarov, Floris van Breugel, J. Nathan Kutz
2025	J	`jnl`	A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning. J. Mach. Learn. Res. Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton
2025	J	`jnl`	A joint optimization approach to identifying sparse dynamics using least squares kernel collocation. CoRR Alexander W. Hsu, Ike Griss Salas, Jacob Stevens-Haas, J. Nathan Kutz, Aleksandr Y. Aravkin, Bamdad Hosseini
2025	J	`jnl`	Accelerating scientific discovery with the common task framework. CoRR J. Nathan Kutz, Peter W. Battaglia, Michael P. Brenner, Kevin Carlberg, Aric A. Hagberg, Shirley Ho, Stephan Hoyer, Henning Lange, Hod Lipson, Michael W. Mahoney, Frank Noé, Max Welling, Laure Zanna, Francis Zhu, Steven L. Brunton
2025	J	`jnl`	Arousal as a universal embedding for spatiotemporal brain dynamics. Nat. Ryan V. Raut, Zachary P. Rosenthal, Xiaodan Wang, Hanyang Miao, Zhanqi Zhang, Jin-Moo Lee, Marcus E. Raichle, Adam Q. Bauer, Steven L. Brunton, Bingni W. Brunton, J. Nathan Kutz
2025	J	`jnl`	Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity. CoRR Ziyu Lu, Anna J. Li, Alexander E. Ladd, Pascha Matveev, Aditya Deole, Eric Shea-Brown, J. Nathan Kutz, Nicholas A. Steinmetz
2025	J	`jnl`	CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics. CoRR Romulo B. da Silva, Diego Passos, Cassio M. Oishi, J. Nathan Kutz
2025	J	`jnl`	Coarse graining and reduced order models for plume ejection dynamics. CoRR Ike Griss Salas, Megan R. Ebers, Jake Stevens-Haas, J. Nathan Kutz
2025	J	`jnl`	Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms. CoRR Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov, Yue Zhao, Stefano Riva, Jan P. Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles D. Cranmer, J. Nathan Kutz
2025	J	`jnl`	Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor. CoRR Stefano Riva, Carolina Introini, J. Nathan Kutz, Antonio Cammi
2025	J	`jnl`	Data assimilation and discrepancy modeling with shallow recurrent decoders. CoRR Yuxuan Bao, J. Nathan Kutz
2025	J	`jnl`	Exploring Human-AI Conceptual Alignment through the Prism of Chess. CoRR Semyon Lomaso, Judah Goldfeder, Mehmet Hamza Erol, Matthew So, Yao Yan, Addison Howard, J. Nathan Kutz, Ravid Shwartz-Ziv
2025	J	`jnl`	From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility. CoRR Carolina Introini, Stefano Riva, J. Nathan Kutz, Antonio Cammi
2025	J	`jnl`	Information theory and discriminative sampling for model discovery. CoRR Yuxuan Bao, J. Nathan Kutz
2025	J	`jnl`	Inverse Design with Dynamic Mode Decomposition. CoRR Yunpeng Zhu, Liangliang Cheng, Anping Jing, Hanyu Huo, Ziqiang Lang, Bo Zhang, J. Nathan Kutz
2025	J	`jnl`	Machine Learning for Sparse Nonlinear Modeling and Control. Annu. Rev. Control. Robotics Auton. Syst. Steven L. Brunton, Nicholas Zolman, J. Nathan Kutz, Urban Fasel
2025	J	`jnl`	Mesh-free sparse identification of nonlinear dynamics. CoRR Mars Liyao Gao, J. Nathan Kutz, Bernat Font
2025	—	`conf`	Physics-Enforced Reservoir Computing for Forecasting Spatiotemporal Systems. L4DC Dima Tretiak, Anastasia S. Bizyaeva, J. Nathan Kutz, Steven L. Brunton
2025	J	`jnl`	PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery. CoRR David Ye, Jan P. Williams, Mars Liyao Gao, Stefano Riva, Matteo Tomasetto, David Zoro, J. Nathan Kutz
2025	J	`jnl`	PySensors 2.0: A Python Package for Sparse Sensor Placement. CoRR Niharika Karnik, Yash Bhangale, Mohammad G. Abdo, Andrei A. Klishin, Joshua J. Cogliati, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton, Krithika Manohar
2025	J	`jnl`	Reduced Order Modeling with Shallow Recurrent Decoder Networks. CoRR Matteo Tomasetto, Jan P. Williams, Francesco Braghin, Andrea Manzoni, J. Nathan Kutz
2025	J	`jnl`	Shallow recurrent decoder for reduced order modeling of Mach. Learn. Sci. Technol. Farbod Faraji, Maryam Reza, J. Nathan Kutz
2025	J	`jnl`	Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks. CoRR Mars Liyao Gao, Jan P. Williams, J. Nathan Kutz
2025	J	`jnl`	Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures. CoRR Sara M. Ichinaga, Steven L. Brunton, Aleksandr Y. Aravkin, J. Nathan Kutz
2025	J	`jnl`	T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders. CoRR Alexey Yermakov, David Zoro, Mars Liyao Gao, J. Nathan Kutz
2025	J	`jnl`	The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS). CoRR Andrew Ferguson, Marisa Lafleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille L. Bilodeau, Keith Brown, Emmanuel J. Candès, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor W. Coley, Andrew J. Connolly, Fred Daum, Sijia S. Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gómez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutierrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip C. Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos K. Katsaggelos, Jeremy Kepner, Yannis G. Kevrekidis, Michelle P. Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya B. Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant M. Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, René Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang
2025	J	`jnl`	The Seismic Wavefield Common Task Framework. CoRR Alexey Yermakov, Yue Zhao, Marine Denolle, Yiyu Ni, Philippe Martin Wyder, Judah Goldfeder, Stefano Riva, Jan P. Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles D. Cranmer, J. Nathan Kutz
2025	J	`jnl`	Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks. CoRR Stefano Riva, Carolina Introini, J. Nathan Kutz, Antonio Cammi
2024	J	`jnl`	AI Assistants for Spaceflight Procedures: Combining Generative Pre-Trained Transformer and Retrieval-Augmented Generation on Knowledge Graphs With Augmented Reality Cues. CoRR Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling, Bernd Bewer, Sophie F. Jentzsch, Tobias Hecking, J. Nathan Kutz
2024	J	`jnl`	AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data-driven control. AI Mag. J. Nathan Kutz, Steven L. Brunton, Krithika Manohar, Hod Lipson, Na Li
2024	J	`jnl`	Automating the Practice of Science - Opportunities, Challenges, and Implications. CoRR Sebastian Musslick, Laura Bartlett, Suyog H. Chandramouli, Marina Dubova, Fernand Gobet, Thomas L. Griffiths, Jessica Hullman, Ross D. King, J. Nathan Kutz, Christopher G. Lucas, Suhas Mahesh, Franco Pestilli, Sabina J. Sloman, William R. Holmes
2024	J	`jnl`	Data-driven local operator finding for reduced-order modelling of plasma systems: I. Concept and verifications. CoRR Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz
2024	J	`jnl`	Data-driven local operator finding for reduced-order modelling of plasma systems: II. Application to parametric dynamics. CoRR Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz
2024	J	`jnl`	Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems. CoRR Doris Voina, Steven L. Brunton, J. Nathan Kutz
2024	J	`jnl`	Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects. SIAM J. Appl. Dyn. Syst. Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz
2024	J	`jnl`	Ensemble Principal Component Analysis. IEEE Access Olga Dorabiala, Aleksandr Y. Aravkin, J. Nathan Kutz
2024	J	`jnl`	Learning Nonlinear Dynamics Using Kalman Smoothing. IEEE Access Jacob Stevens-Haas, Yash Bhangale, J. Nathan Kutz, Aleksandr Y. Aravkin
2024	J	`jnl`	Leveraging Arbitrary Mobile Sensor Trajectories With Shallow Recurrent Decoder Networks for Full-State Reconstruction. IEEE Access Megan R. Ebers, Jan P. Williams, Katherine M. Steele, J. Nathan Kutz
2024	J	`jnl`	Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation. Sensors Jiazhong Mei, Steven L. Brunton, J. Nathan Kutz
2024	J	`jnl`	Motif distribution and function of sparse deep neural networks. CoRR Olivia T. Zahn, Thomas L. Daniel, J. Nathan Kutz
2024	J	`jnl`	Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks. CoRR Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
2024	J	`jnl`	Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data. CoRR Meghana Velegar, Christoph Keller, J. Nathan Kutz
2024	J	`jnl`	Promising directions of machine learning for partial differential equations. Nat. Comput. Sci. Steven L. Brunton, J. Nathan Kutz
2024	J	`jnl`	PyDMD: A Python Package for Robust Dynamic Mode Decomposition. J. Mach. Learn. Res. Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, J. Nathan Kutz
2024	J	`jnl`	PyDMD: A Python package for robust dynamic mode decomposition. CoRR Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, J. Nathan Kutz
2024	J	`jnl`	PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. J. Open Source Softw. Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton
2024	J	`jnl`	Reservoir computing for system identification and predictive control with limited data. CoRR Jan P. Williams, J. Nathan Kutz, Krithika Manohar
2024	J	`jnl`	Robust State Estimation from Partial Out-Core Measurements with Shallow Recurrent Decoder for Nuclear Reactors. CoRR Stefano Riva, Carolina Introini, Antonio Cammi, J. Nathan Kutz
2024	J	`jnl`	SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning. CoRR Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Brunton
2024	J	`jnl`	Shallow Recurrent Decoder for Reduced Order Modeling of Plasma Dynamics. CoRR J. Nathan Kutz, Maryam Reza, Farbod Faraji, Aaron Knoll
2024	Misc	`conf`	Single-Pixel Imaging Of Dynamic Flows Using Neural Ode Regularization. ICASSP Aleksei Sholokhov, Joshua Rapp, Saleh Nabi, Steven L. Brunton, J. Nathan Kutz, Hassan Mansour
2024	J	`jnl`	Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics. IEEE Trans. Computational Imaging Aleksei Sholokhov, Saleh Nabi, Joshua Rapp, Steven L. Brunton, J. Nathan Kutz, Petros T. Boufounos, Hassan Mansour
2024	J	`jnl`	Solving Nonlinear Ordinary Differential Equations Using the Invariant Manifolds and Koopman Eigenfunctions. SIAM J. Appl. Dyn. Syst. Megan Morrison, J. Nathan Kutz
2024	J	`jnl`	Statistical Mechanics of Dynamical System Identification. CoRR Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz, Krithika Manohar
2024	J	`jnl`	Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight. CoRR Oliver Bensch, Leonie Bensch, Tommy Nilsson, Florian Saling, Wafa Sadri, Carsten Hartmann, Tobias Hecking, J. Nathan Kutz
2024	J	`jnl`	VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. CoRR Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
2023	J	`jnl`	A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning. CoRR Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton
2023	J	`jnl`	Attention for Causal Relationship Discovery from Biological Neural Dynamics. CoRR Ziyu Lu, Anika Tabassum, Shruti R. Kulkarni, Lu Mi, J. Nathan Kutz, Eric Shea-Brown, Seung-Hwan Lim
2023	J	`jnl`	Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery. CoRR L. Mars Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz
2023	J	`jnl`	Data-Driven Discovery of Governing Equations for Coarse-Grained Heterogeneous Network Dynamics. SIAM J. Appl. Dyn. Syst. Katherine Owens, J. Nathan Kutz
2023	J	`jnl`	Data-Induced Interactions of Sparse Sensors. CoRR Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar
2023	J	`jnl`	Deep Learning Based Object Tracking in Walking Droplet and Granular Intruder Experiments. CoRR Erdi Kara, George Zhang, Joseph J. Williams, Gonzalo Ferrandez-Quinto, Leviticus J. Rhoden, Maximilian Kim, J. Nathan Kutz, Aminur Rahman
2023	J	`jnl`	Deep learning based object tracking in walking droplet and granular intruder experiments. J. Real Time Image Process. Erdi Kara, George Zhang, Joseph J. Williams, Gonzalo Ferrandez-Quinto, Leviticus J. Rhoden, Maximilian Kim, J. Nathan Kutz, Aminur Rahman
2023	J	`jnl`	Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: I. Extraction of spatiotemporally coherent patterns. CoRR Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz
2023	J	`jnl`	Dynamic Mode Decomposition for data-driven analysis and reduced-order modelling of ExB plasmas: II. dynamics forecasting. CoRR Farbod Faraji, Maryam Reza, Aaron Knoll, J. Nathan Kutz
2023	Misc	`conf`	Extremum Seeking Control of Quantum Gates. QCE Erfan Abbasgholinejad, Haoqin Deng, John King Gamble, J. Nathan Kutz, Erik Nielsen, Neal C. Pisenti, Ningzhi Xie
2023	J	`jnl`	HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations. CoRR Mozes Jacobs, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz, Ryan V. Raut
2023	J	`jnl`	Koopman-inspired approach for identification of exogenous anomalies in nonstationary time-series data. Mach. Learn. Sci. Technol. Alex Mallen, Christoph A. Keller, J. Nathan Kutz
2023	J	`jnl`	Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction. CoRR Megan R. Ebers, Jan P. Williams, Katherine M. Steele, J. Nathan Kutz
2023	J	`jnl`	Machine Learning for Partial Differential Equations. CoRR Steven L. Brunton, J. Nathan Kutz
2023	J	`jnl`	Multi-fidelity reduced-order surrogate modeling. CoRR Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz
2023	J	`jnl`	Multiresolution convolutional autoencoders. J. Comput. Phys. Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz
2023	J	`jnl`	Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. J. Mach. Learn. Res. Shaowu Pan, Steven L. Brunton, J. Nathan Kutz
2023	J	`jnl`	Non-Stationary Dynamic Mode Decomposition. IEEE Access John Ferré, Ariel Rokem, Elizabeth A. Buffalo, J. Nathan Kutz, Adrienne Fairhall
2023	J	`jnl`	Nonlinear parametric models of viscoelastic fluid flows. CoRR Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz, Steven L. Brunton
2023	—	`conf`	Observability-Based Energy Efficient Path Planning with Background Flow via Deep Reinforcement Learning. CDC Jiazhong Mei, J. Nathan Kutz, Steven L. Brunton
2023	J	`jnl`	PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. CoRR Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton
2023	A*	`conf`	Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC. ICRA Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How
2023	J	`jnl`	The Adaptive Spectral Koopman Method for Dynamical Systems. SIAM J. Appl. Dyn. Syst. Bian Li, Yi-An Ma, J. Nathan Kutz, Xiu Yang
2023	J	`jnl`	Transitions between peace and systemic war as bifurcations in a signed network dynamical system. Netw. Sci. Megan Morrison, J. Nathan Kutz, Michael Gabbay
2023	J	`jnl`	Walking Droplets as a Damped-Driven System. SIAM J. Appl. Dyn. Syst. Aminur Rahman, J. Nathan Kutz
2022	J	`jnl`	A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes. IEEE Access Charles B. Delahunt, J. Nathan Kutz
2022	J	`jnl`	Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data. Mach. Learn. Sci. Technol. Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz
2022	J	`jnl`	Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. CoRR L. Mars Gao, J. Nathan Kutz
2022	J	`jnl`	Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 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
2022	J	`jnl`	Dimensionally Consistent Learning with Buckingham Pi. CoRR Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz
2022	J	`jnl`	Dimensionally consistent learning with Buckingham Pi. Nat. Comput. Sci. Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz
2022	J	`jnl`	Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders. CoRR Joseph Bakarji, Kathleen P. Champion, J. Nathan Kutz, Steven L. Brunton
2022	J	`jnl`	Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and Hyperparameters for Optimized, Explainable Machine Learning. Frontiers Artif. Intell. Nina de Lacy, Michael J. Ramshaw, J. Nathan Kutz
2022	J	`jnl`	Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data. CoRR Alex Mallen, Christoph A. Keller, J. Nathan Kutz
2022	J	`jnl`	Machine Learning in Heterogeneous Porous Materials. CoRR 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
2022	J	`jnl`	Model predictive control for robust quantum state preparation. Quantum Andy Goldschmidt, Jonathan L. Dubois, Steven L. Brunton, J. Nathan Kutz
2022	J	`jnl`	Modern Koopman Theory for Dynamical Systems. SIAM Rev. Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz
2022	J	`jnl`	Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. CoRR Shaowu Pan, Steven L. Brunton, J. Nathan Kutz
2022	J	`jnl`	Optimal Sensor and Actuator Selection Using Balanced Model Reduction. IEEE Trans. Autom. Control. Krithika Manohar, J. Nathan Kutz, Steven L. Brunton
2022	J	`jnl`	Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight. PLoS Comput. Biol. Olivia Zahn, Jorge Bustamante, Callin Switzer, Thomas L. Daniel, J. Nathan Kutz
2022	J	`jnl`	PyNumDiff: A Python package for numerical differentiation of noisy time-series data. J. Open Source Softw. Floris van Breugel, Yuying Liu, Bingni W. Brunton, J. Nathan Kutz
2022	J	`jnl`	PySINDy: A comprehensive Python package for robust sparse system identification. J. Open Source Softw. 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
2022	J	`jnl`	Robust and Scalable Methods for the Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. Travis Askham, Peng Zheng, Aleksandr Y. Aravkin, J. Nathan Kutz
2022	J	`jnl`	Robust trimmed k-means. Pattern Recognit. Lett. Olga Dorabiala, J. Nathan Kutz, Aleksandr Y. Aravkin
2022	J	`jnl`	Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC. CoRR Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How
2022	J	`jnl`	Spatiotemporal k-means. CoRR Olga Dorabiala, Jennifer Webster, J. Nathan Kutz, Aleksandr Y. Aravkin
2022	J	`jnl`	Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems. IEEE Access Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz
2022	J	`jnl`	The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control. CoRR Kadierdan Kaheman, Urban Fasel, Jason J. Bramburger, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton
2022	J	`jnl`	Transitions between peace and systemic war as bifurcations in a signed network dynamical system. CoRR Megan Morrison, J. Nathan Kutz, Michael Gabbay
2021	J	`jnl`	A toolkit for data-driven discovery of governing equations in high-noise regimes. CoRR Charles B. Delahunt, J. Nathan Kutz
2021	J	`jnl`	Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantification. CoRR Diya Sashidhar, J. Nathan Kutz
2021	J	`jnl`	Data-Driven Stabilization of Periodic Orbits. IEEE Access Jason J. Bramburger, J. Nathan Kutz, Steven L. Brunton
2021	J	`jnl`	Data-driven discovery of Koopman eigenfunctions for control. Mach. Learn. Sci. Technol. Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton
2021	J	`jnl`	Deep Learning of Conjugate Mappings. CoRR Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz
2021	J	`jnl`	Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties. CoRR Alex Mallen, Henning Lange, J. Nathan Kutz
2021	J	`jnl`	DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems. CoRR Craig R. Gin, Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz
2021	J	`jnl`	Deeptime: a Python library for machine learning dynamical models from time series data. CoRR 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
2021	J	`jnl`	Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control. CoRR Urban Fasel, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton
2021	J	`jnl`	Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data. IEEE Access Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz
2021	J	`jnl`	Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data. CoRR Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz
2021	J	`jnl`	FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics. CoRR Henning Lange, J. Nathan Kutz
2021	J	`jnl`	From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. J. Mach. Learn. Res. Henning Lange, Steven L. Brunton, J. Nathan Kutz
2021	J	`jnl`	Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations. CoRR Manu Kalia, Steven L. Brunton, Hil G. E. Meijer, Christoph Brune, J. Nathan Kutz
2021	J	`jnl`	Modern Koopman Theory for Dynamical Systems. CoRR Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz
2021	J	`jnl`	Nonlinear Control of Networked Dynamical Systems. IEEE Trans. Netw. Sci. Eng. Megan Morrison, J. Nathan Kutz
2021	J	`jnl`	Physics-informed dynamic mode decomposition (piDMD). CoRR Peter J. Baddoo, Benjamin Herrmann, Beverley J. McKeon, J. Nathan Kutz, Steven L. Brunton
2021	J	`jnl`	PySINDy: A comprehensive Python package for robust sparse system identification. CoRR 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
2021	J	`jnl`	PySensors: A Python Package for Sparse Sensor Placement. CoRR Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz
2021	J	`jnl`	PySensors: A Python package for sparse sensor placement. J. Open Source Softw. Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton
2021	J	`jnl`	Robust Trimmed k-means. CoRR Olga Dorabiala, J. Nathan Kutz, Aleksandr Y. Aravkin
2021	—	`conf`	SINDy with Control: A Tutorial. CDC Urban Fasel, Eurika Kaiser, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton
2020	J	`jnl`	A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data. IEEE Access Kathleen P. Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data. CoRR Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	Bracketing brackets with bras and kets. CoRR Emily Clark, Angelie Vincent, J. Nathan Kutz, Steven L. Brunton
2020	J	`jnl`	Centering Data Improves the Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. Seth M. Hirsh, Kameron Decker Harris, J. Nathan Kutz, Bingni W. Brunton
2020	J	`jnl`	Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning. CoRR 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
2020	J	`jnl`	Deep reinforcement learning for optical systems: A case study of mode-locked lasers. Mach. Learn. Sci. Technol. Chang Sun, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	Discovery of Physics From Data: Universal Laws and Discrepancies. Frontiers Artif. Intell. Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	Dynamic mode decomposition for forecasting and analysis of power grid load data. CoRR Daniel Dylewsky, David Barajas-Solano, Tong Ma, Alexandre M. Tartakovsky, J. Nathan Kutz
2020	J	`jnl`	From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. CoRR Henning Lange, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers. CoRR Yuying Liu, J. Nathan Kutz, Steven L. Brunton
2020	J	`jnl`	Multiresolution Convolutional Autoencoders. CoRR Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	Nonlinear Control in the Nematode C. elegans. Frontiers Comput. Neurosci. Megan Morrison, Charles Fieseler, J. Nathan Kutz
2020	J	`jnl`	Numerical Differentiation of Noisy Data: A Unifying Multi-Objective Optimization Framework. IEEE Access Floris van Breugel, J. Nathan Kutz, Bingni W. Brunton
2020	J	`jnl`	Physics-informed machine learning for sensor fault detection with flight test data. CoRR 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
2020	J	`jnl`	Principal Component Trajectories (PCT): Nonlinear dynamics as a superposition of time-delayed periodic orbits. CoRR Daniel Dylewsky, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data. J. Open Source Softw. Brian de Silva, Kathleen P. Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton
2020	J	`jnl`	Randomized CP tensor decomposition. Mach. Learn. Sci. Technol. N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems. CoRR Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz
2020	J	`jnl`	SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics. CoRR Kadierdan Kaheman, J. Nathan Kutz, Steven L. Brunton
2020	J	`jnl`	Sparse Identification of Slow Timescale Dynamics. CoRR Jason J. Bramburger, Daniel Dylewsky, J. Nathan Kutz
2020	J	`jnl`	Sparse Principal Component Analysis via Variable Projection. SIAM J. Appl. Math. N. Benjamin Erichson, Peng Zheng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
2020	J	`jnl`	Time-Delay Observables for Koopman: Theory and Applications. SIAM J. Appl. Dyn. Syst. Mason Kamb, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
2019	J	`jnl`	A Unified Framework for Sparse Relaxed Regularized Regression: SR3. IEEE Access Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
2019	J	`jnl`	A unified sparse optimization framework to learn parsimonious physics-informed models from data. CoRR Kathleen P. Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz
2019	J	`jnl`	Complex Algorithms for Data-Driven Model Learning in Science and Engineering. Complex. Francisco Javier Montáns, Francisco Chinesta, Rafael Gómez-Bombarelli, J. Nathan Kutz
2019	J	`jnl`	Compressed dynamic mode decomposition for background modeling. J. Real Time Image Process. N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz
2019	J	`jnl`	Data-Driven Identification of Parametric Partial Differential Equations. SIAM J. Appl. Dyn. Syst. Samuel H. Rudy, Alessandro Alla, Steven L. Brunton, J. Nathan Kutz
2019	J	`jnl`	Data-driven multiscale decompositions for forecasting and model discovery. CoRR Daniel Dylewsky, Molei Tao, J. Nathan Kutz
2019	J	`jnl`	Deep Learning Models for Global Coordinate Transformations that Linearize PDEs. CoRR Craig Gin, Bethany Lusch, Steven L. Brunton, J. Nathan Kutz
2019	J	`jnl`	Deep Model Predictive Control with Online Learning for Complex Physical Systems. CoRR Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz
2019	J	`jnl`	Deep learning of dynamics and signal-noise decomposition with time-stepping constraints. J. Comput. Phys. Samuel H. Rudy, J. Nathan Kutz, Steven L. Brunton
2019	J	`jnl`	Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings. SIAM J. Appl. Dyn. Syst. Kathleen P. Champion, Steven L. Brunton, J. Nathan Kutz
2019	J	`jnl`	Discovery of Physics from Data: Universal Laws and Discrepancy Models. CoRR Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz
2019	J	`jnl`	Engineering structural robustness in power grid networks susceptible to community desynchronization. Appl. Netw. Sci. Daniel Dylewsky, Xiu Yang, Alexandre M. Tartakovsky, J. Nathan Kutz
2019	J	`jnl`	Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition. Frontiers Comput. Neurosci. James M. Kunert-Graf, Kristian M. Eschenburg, David J. Galas, J. Nathan Kutz, Swati D. Rane, Bingni W. Brunton
2019	J	`jnl`	Learning Discrepancy Models From Experimental Data. CoRR Kadierdan Kaheman, Eurika Kaiser, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton
2019	J	`jnl`	Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy. CoRR Charles B. Delahunt, Courosh Mehanian, J. Nathan Kutz
2019	J	`jnl`	Optimized Sampling for Multiscale Dynamics. Multiscale Model. Simul. Krithika Manohar, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
2019	J	`jnl`	Putting a bug in ML: The moth olfactory network learns to read MNIST. Neural Networks Charles B. Delahunt, J. Nathan Kutz
2019	J	`jnl`	Randomized Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. N. Benjamin Erichson, Lionel Mathelin, J. Nathan Kutz, Steven L. Brunton
2019	J	`jnl`	Randomized model order reduction. Adv. Comput. Math. Alessandro Alla, J. Nathan Kutz
2019	J	`jnl`	Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates. NeuroImage Nina de Lacy, Elizabeth McCauley, J. Nathan Kutz, Vince D. Calhoun
2019	J	`jnl`	Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. CoRR N. Benjamin Erichson, Lionel Mathelin, Zhewei Yao, Steven L. Brunton, Michael W. Mahoney, J. Nathan Kutz
2019	B	`conf`	Shape Constrained Tensor Decompositions. DSAA Bethany Lusch, Eric C. Chi, J. Nathan Kutz
2019	J	`jnl`	Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries. J. Comput. Neurosci. Pedro D. Maia, Ashish Raj, J. Nathan Kutz
2019	J	`jnl`	Smoothing and parameter estimation by soft-adherence to governing equations. J. Comput. Phys. Samuel H. Rudy, Steven L. Brunton, J. Nathan Kutz
2018	—	`conf`	A moth brain learns to read MNIST. ICLR (Workshop) Charles B. Delahunt, J. Nathan Kutz
2018	J	`jnl`	Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems. Complex. J. Nathan Kutz, Joshua L. Proctor, Steven L. Brunton
2018	J	`jnl`	Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, With Applications to Neural Nets. Frontiers Comput. Neurosci. Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz
2018	J	`jnl`	Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets. CoRR Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz
2018	J	`jnl`	Diffusion Maps meet Nyström. CoRR N. Benjamin Erichson, Lionel Mathelin, Steven L. Brunton, J. Nathan Kutz
2018	—	`conf`	Discovering Conservation Laws from Data for Control. CDC Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton
2018	J	`jnl`	Discovering conservation laws from data for control. CoRR Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton
2018	J	`jnl`	Generalizing Koopman Theory to Allow for Inputs and Control. SIAM J. Appl. Dyn. Syst. Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz
2018	J	`jnl`	Insect cyborgs: Biological feature generators improve machine learning accuracy on limited data. CoRR Charles B. Delahunt, J. Nathan Kutz
2018	J	`jnl`	Online Interpolation Point Refinement for Reduced-Order Models using a Genetic Algorithm. SIAM J. Sci. Comput. Syuzanna Sargsyan, Steven L. Brunton, J. Nathan Kutz
2018	J	`jnl`	Optimal Sensor and Actuator Placement using Balanced Model Reduction. CoRR Krithika Manohar, J. Nathan Kutz, Steven L. Brunton
2018	J	`jnl`	Putting a bug in ML: The moth olfactory network learns to read MNIST. CoRR Charles B. Delahunt, J. Nathan Kutz
2018	J	`jnl`	Randomized nonnegative matrix factorization. Pattern Recognit. Lett. N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz
2018	J	`jnl`	Sparse Principal Component Analysis via Variable Projection. CoRR N. Benjamin Erichson, Peng Zeng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
2018	J	`jnl`	Sparse Relaxed Regularized Regression: SR3. CoRR Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
2018	J	`jnl`	Sparsity enabled cluster reduced-order models for control. J. Comput. Phys. Eurika Kaiser, Marek Morzynski, Guillaume Daviller, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton
2018	J	`jnl`	Structural Load Estimation Using Machine Vision and Surface Crack Patterns for Shear-Critical RC Beams and Slabs. J. Comput. Civ. Eng. Rouzbeh Davoudi, Gregory R. Miller, J. Nathan Kutz
2018	J	`jnl`	Variable Projection Methods for an Optimized Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. Travis Askham, J. Nathan Kutz
2017	—	`conf`	Compressed Singular Value Decomposition for Image and Video Processing. ICCV Workshops N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz
2017	J	`jnl`	Data-Driven Sparse Sensor Placement. CoRR Krithika Manohar, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton
2017	—	`conf`	Data-Driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology. CAMSAP J. Nathan Kutz, Samuel H. Rudy, Alessandro Alla, Steven L. Brunton
2017	J	`jnl`	Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers. CoRR Thomas Baumeister, Steven L. Brunton, J. Nathan Kutz
2017	J	`jnl`	Deep learning for universal linear embeddings of nonlinear dynamics. CoRR Bethany Lusch, J. Nathan Kutz, Steven L. Brunton
2017	—	`conf`	Dynamic Mode Decomposition for Background Modeling. ICCV Workshops Seth D. Pendergrass, Steven L. Brunton, J. Nathan Kutz, N. Benjamin Erichson, Travis Askham
2017	J	`jnl`	Dynamic mode decomposition for compressive system identification. CoRR Zhe Bai, Eurika Kaiser, Joshua L. Proctor, J. Nathan Kutz, Steven L. Brunton
2017	J	`jnl`	Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage. PLoS Comput. Biol. James M. Kunert, Pedro D. Maia, J. Nathan Kutz
2017	—	`conf`	Machine learning and air quality modeling. IEEE BigData Christoph A. Keller, Mathew J. Evans, J. Nathan Kutz, Steven Pawson
2017	J	`jnl`	Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics. Frontiers Comput. Neurosci. James M. Kunert-Graf, Eli Shlizerman, Andrew Walker, J. Nathan Kutz
2017	J	`jnl`	Nonlinear Model Order Reduction via Dynamic Mode Decomposition. SIAM J. Sci. Comput. Alessandro Alla, J. Nathan Kutz
2017	J	`jnl`	Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External Microelectronics. Comput. Math. Methods Medicine Megan Morrison, Pedro D. Maia, J. Nathan Kutz
2017	J	`jnl`	Randomized CP Tensor Decomposition. CoRR N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz
2017	J	`jnl`	Randomized Dynamic Mode Decomposition. CoRR N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz
2017	J	`jnl`	Randomized Nonnegative Matrix Factorization. CoRR N. Benjamin Erichson, Ariana Mendible, Sophie Wihlborn, J. Nathan Kutz
2017	J	`jnl`	Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurological diseases. J. Comput. Neurosci. Pedro D. Maia, J. Nathan Kutz
2017	J	`jnl`	Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion. PLoS Comput. Biol. James M. Kunert, Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz
2016	J	`jnl`	Classification of Spatiotemporal Data via Asynchronous Sparse Sampling: Application to Flow around a Cylinder. Multiscale Model. Simul. Ido Bright, Guang Lin, J. Nathan Kutz
2016	J	`jnl`	Dynamic Mode Decomposition with Control. SIAM J. Appl. Dyn. Syst. Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz
2016	—	`book`	Dynamic mode decomposition - data-driven modeling of complex systems. J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor
2016	J	`jnl`	Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics. IEEE Trans. Mol. Biol. Multi Scale Commun. Niall M. Mangan, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz
2016	J	`jnl`	Multiresolution Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. J. Nathan Kutz, Xing Fu, Steven L. Brunton
2016	J	`jnl`	Online interpolation point refinement for reduced order models using a genetic algorithm. CoRR Syuzanna Sargsyan, Steven L. Brunton, J. Nathan Kutz
2016	J	`jnl`	Randomized Matrix Decompositions using R. CoRR N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz
2016	J	`jnl`	Shape Constrained Tensor Decompositions using Sparse Representations in Over-Complete Libraries. CoRR Bethany Lusch, Eric C. Chi, J. Nathan Kutz
2016	J	`jnl`	Sparse Sensor Placement Optimization for Classification. SIAM J. Appl. Math. Bingni W. Brunton, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz
2016	J	`jnl`	Streaming GPU Singular Value and Dynamic Mode Decompositions. CoRR Seth D. Pendergrass, J. Nathan Kutz, Steven L. Brunton
2015	J	`jnl`	Compressed Dynamic Mode Decomposition for Real-Time Object Detection. CoRR N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz
2015	—	`conf`	Multi-resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking. ICCV Workshops J. Nathan Kutz, Xing Fu, Steven L. Brunton, N. Benjamin Erichson
2014	J	`jnl`	A Reaction-Diffusion Model of Cholinergic Retinal Waves. PLoS Comput. Biol. Benjamin Lansdell, Kevin Ford, J. Nathan Kutz
2014	J	`jnl`	Compressive Sensing and Low-Rank Libraries for Classification of Bifurcation Regimes in Nonlinear Dynamical Systems. SIAM J. Appl. Dyn. Syst. Steven L. Brunton, Jonathan H. Tu, Ido Bright, J. Nathan Kutz
2014	J	`jnl`	Compromised axonal functionality after neurodegeneration, concussion and/or traumatic brain injury. J. Comput. Neurosci. Pedro D. Maia, J. Nathan Kutz
2014	J	`jnl`	Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe. Frontiers Comput. Neurosci. Eli Shlizerman, Jeffrey Riffell, J. Nathan Kutz
2014	J	`jnl`	Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video. CoRR Jacob Grosek, J. Nathan Kutz
2014	J	`jnl`	Herpes Simplex Virus-2 Genital Tract Shedding Is Not Predictable over Months or Years in Infected Persons. PLoS Comput. Biol. Varsha Dhankani, J. Nathan Kutz, Joshua T. Schiffer
2014	J	`jnl`	Identifying critical regions for spike propagation in axon segments. J. Comput. Neurosci. Pedro D. Maia, J. Nathan Kutz
2014	J	`jnl`	Selecting a Small Set of Optimal Gestures from an Extensive Lexicon. CoRR Jacob Grosek, J. Nathan Kutz
2013	J	`jnl`	Erratum: Hybrid Reduced-Order Integration with Proper Orthogonal Decomposition and Dynamic Mode Decomposition. Multiscale Model. Simul. Matthew O. Williams, Peter J. Schmid, J. Nathan Kutz
2013	J	`jnl`	Hybrid Reduced-Order Integration with Proper Orthogonal Decomposition and Dynamic Mode Decomposition. Multiscale Model. Simul. Matthew O. Williams, Peter J. Schmid, J. Nathan Kutz
2013	J	`jnl`	Optimal Sensor Placement and Enhanced Sparsity for Classification. CoRR Bingni W. Brunton, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz
2013	—	`conf`	Theoretical studies of frequency domain mode-locked fiber lasers. ICAIT Feng Li, J. Nathan Kutz, Alexander Ping-Kong Wai
2012	J	`jnl`	Neural Activity Measures and Their Dynamics. SIAM J. Appl. Math. Eli Shlizerman, Konrad Schroder, J. Nathan Kutz
2012	J	`jnl`	The Low Dimensionality of Time-Periodic Standing Waves in Water of Finite and Infinite Depth. SIAM J. Appl. Dyn. Syst. Matthew O. Williams, Eli Shlizerman, Jon Wilkening, J. Nathan Kutz
2007	J	`jnl`	Averaged models for passive mode-locking using nonlinear mode-coupling. Math. Comput. Simul. Joshua L. Proctor, J. Nathan Kutz
2007	J	`jnl`	SpectrUW: A laboratory for the numerical exploration of spectra of linear operators. Math. Comput. Simul. Bernard Deconinck, Firat Kiyak, John D. Carter, J. Nathan Kutz
2006	J	`jnl`	Computing spectra of linear operators using the Floquet-Fourier-Hill method. J. Comput. Phys. Bernard Deconinck, J. Nathan Kutz
2006	J	`jnl`	Mode-Locked Soliton Lasers. SIAM Rev. J. Nathan Kutz
2005	J	`jnl`	Dynamics of the Optical Parametric Oscillator Near Resonance Detuning. SIAM J. Appl. Dyn. Syst. Sarah E. Hewitt, J. Nathan Kutz
2002	J	`jnl`	Dynamics and Stability of Bose-Einstein Condensates: The Nonlinear Schrödinger Equation with Periodic Potential. J. Nonlinear Sci. Bernard Deconinck, Bela A. Frigyik, J. Nathan Kutz
1999	J	`jnl`	Dynamics and Bifurcations of a Planar Map Modeling Dispersion Managed Breathers. SIAM J. Appl. Math. J. Nathan Kutz, Philip Holmes
1996	J	`jnl`	Stability of Pulses in Nonlinear Optical Fibers Using Phase-Sensitive Amplifiers. SIAM J. Appl. Math. J. Nathan Kutz, William L. Kath