Steven L. Brunton

According to our database, Steven L. Brunton authored at least 120 papers between 2013 and 2024.

Collaborative distances:

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

2024

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

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

CoRR, 2024

Koopman-Assisted Reinforcement Learning.

Preston Rozwood, Edward Mehrez, Ludger Paehler, Wen Sun, Steven L. Brunton

CoRR, 2024

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

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

CoRR, 2024

PyDMD: A Python package for robust dynamic mode decomposition.

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

CoRR, 2024

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

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

AI Mag., 2024

2023

Data-driven prediction of the performance of enhanced surfaces from an extensive CFD-generated parametric search space.

Ana Larrañaga, Steven L. Brunton, Javier Martínez, Sergio Chapela, Jacobo Porteiro

Mach. Learn. Sci. Technol., June, 2023

Multiresolution convolutional autoencoders.

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

J. Comput. Phys., February, 2023

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

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

J. Mach. Learn. Res., 2023

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

Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements.

Esther Lagemann, Steven L. Brunton, Christian Lagemann

CoRR, 2023

HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations.

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

CoRR, 2023

Multi-fidelity reduced-order surrogate modeling.

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

CoRR, 2023

Nonlinear parametric models of viscoelastic fluid flows.

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

CoRR, 2023

Control of Vortex Dynamics using Invariants.

Kartik Krishna, Aditya G. Nair, Anand Krishnan, Steven L. Brunton, Eurika Kaiser

CoRR, 2023

Optimal Sensor Placement with Adaptive Constraints for Nuclear Digital Twins.

Niharika Karnik, Mohammad G. Abdo, Carlos E. Estrada Perez, Jun Soo Yoo, Joshua J. Cogliati, Richard S. Skifton, Pattrick Calderoni, Steven L. Brunton, Krithika Manohar

CoRR, 2023

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

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

CoRR, 2023

Machine Learning for Partial Differential Equations.

Steven L. Brunton, J. Nathan Kutz

CoRR, 2023

The transformative potential of machine learning for experiments in fluid mechanics.

Ricardo Vinuesa, Steven L. Brunton, Beverley J. McKeon

CoRR, 2023

Benchmarking sparse system identification with low-dimensional chaos.

Alan A. Kaptanoglu, Lanyue Zhang, Zachary G. Nicolaou, Urban Fasel, Steven L. Brunton

CoRR, 2023

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

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

CoRR, 2023

Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning.

Sebastian Peitz, Jan Stenner, Vikas Chidananda, Oliver Wallscheid, Steven L. Brunton, Kunihiko Taira

CoRR, 2023

Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning.

Kartik Krishna, Steven L. Brunton, Zhuoyuan Song

IEEE Access, 2023

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

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

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

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

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

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

Data-Driven Control: Theory and Applications.

Damoon Soudbakhsh, Anuradha M. Annaswamy, Yan Wang, Steven L. Brunton, Joseph E. Gaudio, Heather S. Hussain, Draguna L. Vrabie, Ján Drgona, Dimitar P. Filev

Proceedings of the American Control Conference, 2023

2022

FlexWing-ROM: A matlab framework for data-driven reduced-order modeling of flexible wings.

Urban Fasel, Nicola Fonzi, Andrea Iannelli, Steven L. Brunton

J. Open Source Softw., December, 2022

Model predictive control for robust quantum state preparation.

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

Quantum, September, 2022

Optimal Sensor and Actuator Selection Using Balanced Model Reduction.

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

IEEE Trans. Autom. Control., 2022

Modern Koopman Theory for Dynamical Systems.

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

SIAM Rev., 2022

Enhancing computational fluid dynamics with machine learning.

Ricardo Vinuesa, Steven L. Brunton

Nat. Comput. Sci., 2022

Dimensionally consistent learning with Buckingham Pi.

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

Nat. Comput. Sci., 2022

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

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

Mach. Learn. Sci. Technol., 2022

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

Moritz Hoffmann, Martin Scherer, Tim Hempel, Andreas Mardt, Brian de Silva, Brooke E. Husic, Stefan Klus, Hao Wu, J. Nathan Kutz, Steven L. Brunton, Frank Noé

Mach. Learn. Sci. Technol., 2022

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

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

J. Open Source Softw., 2022

Projection-tree reduced-order modeling for fast <i>N</i>-body computations.

Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos

J. Comput. Phys., 2022

Emerging Trends in Machine Learning for Computational Fluid Dynamics.

Ricardo Vinuesa, Steven L. Brunton

Comput. Sci. Eng., 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

Swarm Modelling with Dynamic Mode Decomposition.

Emma Hansen, Steven L. Brunton, Zhuoyuan Song

CoRR, 2022

Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning.

Said Ouala, Steven L. Brunton, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet

CoRR, 2022

Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders.

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

CoRR, 2022

Swarm Modeling With Dynamic Mode Decomposition.

Emma Hansen, Steven L. Brunton, Zhuoyuan Song

IEEE Access, 2022

Nonlinear System Level Synthesis for Polynomial Dynamical Systems.

Lauren E. Conger, Jing Shuang Lisa Li, Eric Mazumdar, Steven L. Brunton

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021

Data-driven discovery of Koopman eigenfunctions for control.

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

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

PySensors: A Python package for sparse sensor placement.

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

J. Open Source Softw., 2021

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

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

J. Mach. Learn. Res., 2021

Physics-informed dynamic mode decomposition (piDMD).

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

CoRR, 2021

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

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

CoRR, 2021

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

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

CoRR, 2021

The Potential of Machine Learning to Enhance Computational Fluid Dynamics.

Ricardo Vinuesa, Steven L. Brunton

CoRR, 2021

Applying Machine Learning to Study Fluid Mechanics.

Steven L. Brunton

CoRR, 2021

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

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

CoRR, 2021

Deep Learning of Conjugate Mappings.

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

CoRR, 2021

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows.

Kartik Krishna, Zhuoyuan Song, Steven L. Brunton

CoRR, 2021

Projection-tree reduced order modeling for fast N-body computations.

Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos

CoRR, 2021

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

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

CoRR, 2021

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

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

IEEE Access, 2021

Data-Driven Stabilization of Periodic Orbits.

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

IEEE Access, 2021

SINDy with Control: A Tutorial.

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

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

2020

Sparse Principal Component Analysis via Variable Projection.

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

SIAM J. Appl. Math., 2020

Time-Delay Observables for Koopman: Theory and Applications.

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

SIAM J. Appl. Dyn. Syst., 2020

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

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

Mach. Learn. Sci. Technol., 2020

Randomized CP tensor decomposition.

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

Mach. Learn. Sci. Technol., 2020

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

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

J. Open Source Softw., 2020

Discovery of Physics From Data: Universal Laws and Discrepancies.

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

Frontiers Artif. Intell., 2020

Learning Precisely Timed Feedforward Control of the Sensor-Denied Inverted Pendulum.

Thomas L. Mohren, Thomas L. Daniel, Steven L. Brunton

IEEE Control. Syst. Lett., 2020

Bracketing brackets with bras and kets.

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

CoRR, 2020

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

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

CoRR, 2020

Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers.

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

CoRR, 2020

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

Brian M. de Silva, Jared Callaham, Jonathan Jonker, Nicholas Goebel, Jennifer Klemisch, Darren McDonald, Nathan Hicks, J. Nathan Kutz, Steven L. Brunton, Aleksandr Y. Aravkin

CoRR, 2020

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

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

CoRR, 2020

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

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

CoRR, 2020

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

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

CoRR, 2020

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

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

IEEE Access, 2020

2019

RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology.

Chen Gong, N. Benjamin Erichson, John P. Kelly, Laura C. Trutoiu, Brian T. Schowengerdt, Steven L. Brunton, Eric J. Seibel

IEEE Trans. Medical Imaging, 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

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

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

Randomized methods to characterize large-scale vortical flow network.

Zhe Bai, N. Benjamin Erichson, Muralikrishnan Gopalakrishnan Meena, Kunihiko Taira, 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

Machine Learning for Fluid Mechanics.

Steven L. Brunton, Bernd Noack, Petros Koumoutsakos

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

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

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

2018

Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification.

Wei Guo, Krithika Manohar, Steven L. Brunton, Ashis Gopal Banerjee

IEEE Trans. Knowl. Data Eng., 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

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

Optimal Sensor and Actuator Placement using Balanced Model Reduction.

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

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

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

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

Deep learning for universal linear embeddings of nonlinear dynamics.

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

CoRR, 2017

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

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

CoRR, 2017

Dynamic mode decomposition for compressive system identification.

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

CoRR, 2017

Data-Driven Sparse Sensor Placement.

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

CoRR, 2017

Randomized Dynamic Mode Decomposition.

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

CoRR, 2017

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

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

Streaming GPU Singular Value and Dynamic Mode Decompositions.

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

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

Long-time uncertainty propagation using generalized polynomial chaos and flow map composition.

Dirk M. Luchtenburg, Steven L. Brunton, Clarence W. Rowley

J. Comput. Phys., 2014

2013

Optimal Sensor Placement and Enhanced Sparsity for Classification.

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

CoRR, 2013