Steven L. Brunton

ORCID

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 140

Top Venues

CoRR

SIAM J. Appl. Dyn. Syst.

J. Comput. Phys.

IEEE Access

Publications

Preston Rozwood, Edward Mehrez, Ludger Paehler, Wen Sun, Steven L. Brunton
Koopman-Assisted Reinforcement Learning. CoRR (2024)
Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. CoRR (2024)
Steven L. Brunton, J. Nathan Kutz
Promising directions of machine learning for partial differential equations. Nat. Comput. Sci. 4 (7) (2024)
Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. J. Open Source Softw. 9 (96) (2024)
Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Brunton
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning. CoRR (2024)
Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo, Marco Tezzele, Karl Lapo, Gianluigi Rozza, Steven L. Brunton, J. Nathan Kutz
PyDMD: A Python package for robust dynamic mode decomposition. CoRR (2024)
J. Nathan Kutz, Steven L. Brunton, Krithika Manohar, Hod Lipson, Na Li
AI Institute in Dynamic Systems: Developing machine learning and AI tools for scientific discovery, engineering design, and data-driven control. AI Mag. 45 (1) (2024)
Ricardo Vinuesa, Jean Rabault, Hossein Azizpour, Stefan Bauer, Bingni W. Brunton, Arne Elofsson, Elias Jarlebring, Hedvig Kjellström, Stefano Markidis, David Marlevi, Paola Cinnella, Steven L. Brunton
Opportunities for machine learning in scientific discovery. CoRR (2024)
Jiazhong Mei, Steven L. Brunton, J. Nathan Kutz
Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation. Sensors 24 (12) (2024)
Aleksei Sholokhov, Saleh Nabi, Joshua Rapp, Steven L. Brunton, J. Nathan Kutz, Petros T. Boufounos, Hassan Mansour
Single-Pixel Imaging of Spatio-Temporal Flows Using Differentiable Latent Dynamics. IEEE Trans. Computational Imaging 10 (2024)
Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks. CoRR (2024)
Aleksei Sholokhov, Joshua Rapp, Saleh Nabi, Steven L. Brunton, J. Nathan Kutz, Hassan Mansour
Single-Pixel Imaging Of Dynamic Flows Using Neural Ode Regularization. ICASSP (2024)
Shaowu Pan, Steven L. Brunton, J. Nathan Kutz
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. J. Mach. Learn. Res. 24 (2023)
L. Mars Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery. CoRR (2023)
Kartik Krishna, Steven L. Brunton, Zhuoyuan Song
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning. IEEE Access 11 (2023)
Esther Lagemann, Steven L. Brunton, Christian Lagemann
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements. CoRR (2023)
Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC. ICRA (2023)
Kartik Krishna, Aditya G. Nair, Anand Krishnan, Steven L. Brunton, Eurika Kaiser
Control of Vortex Dynamics using Invariants. CoRR (2023)
Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz, Steven L. Brunton
Nonlinear parametric models of viscoelastic fluid flows. CoRR (2023)
A Larrañaga, Steven L. Brunton, J. Martínez, S. Chapela, Jacobo Porteiro
Data-driven prediction of the performance of enhanced surfaces from an extensive CFD-generated parametric search space. Mach. Learn. Sci. Technol. 4 (2) (2023)
Mozes Jacobs, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz, Ryan V. Raut
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations. CoRR (2023)
Shaowu Pan, Eurika Kaiser, Brian M. de Silva, J. Nathan Kutz, Steven L. Brunton
PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator. CoRR (2023)
Alan A. Kaptanoglu, Lanyue Zhang, Zachary G. Nicolaou, Urban Fasel, Steven L. Brunton
Benchmarking sparse system identification with low-dimensional chaos. CoRR (2023)
Ricardo Vinuesa, Steven L. Brunton, Beverley J. McKeon
The transformative potential of machine learning for experiments in fluid mechanics. CoRR (2023)
Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz
Multi-fidelity reduced-order surrogate modeling. CoRR (2023)
Jiazhong Mei, J. Nathan Kutz, Steven L. Brunton
Observability-Based Energy Efficient Path Planning with Background Flow via Deep Reinforcement Learning. CDC (2023)
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
Optimal Sensor Placement with Adaptive Constraints for Nuclear Digital Twins. CoRR (2023)
Damoon Soudbakhsh, Anuradha M. Annaswamy, Yan Wang, Steven L. Brunton, Joseph E. Gaudio, Heather S. Hussain, Draguna L. Vrabie, Ján Drgona, Dimitar P. Filev
Data-Driven Control: Theory and Applications. ACC (2023)
Sebastian Peitz, Jan Stenner, Vikas Chidananda, Oliver Wallscheid, Steven L. Brunton, Kunihiko Taira
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning. CoRR (2023)
Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning. CoRR (2023)
Kartik Krishna, Steven L. Brunton, Zhuoyuan Song
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning. CoRR (2023)
Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz
Multiresolution convolutional autoencoders. J. Comput. Phys. 474 (2023)
Steven L. Brunton, J. Nathan Kutz
Machine Learning for Partial Differential Equations. CoRR (2023)
Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz
Dimensionally Consistent Learning with Buckingham Pi. CoRR (2022)
Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz
Modern Koopman Theory for Dynamical Systems. SIAM Rev. 64 (2) (2022)
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
Deeptime: a Python library for machine learning dynamical models from time series data. Mach. Learn. Sci. Technol. 3 (1) (2022)
Emma Hansen, Steven L. Brunton, Zhuoyuan Song
Swarm Modelling with Dynamic Mode Decomposition. CoRR (2022)
Ricardo Vinuesa, Steven L. Brunton
Emerging Trends in Machine Learning for Computational Fluid Dynamics. Comput. Sci. Eng. 24 (5) (2022)
Joseph Bakarji, Kathleen P. Champion, J. Nathan Kutz, Steven L. Brunton
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders. CoRR (2022)
Krithika Manohar, J. Nathan Kutz, Steven L. Brunton
Optimal Sensor and Actuator Selection Using Balanced Model Reduction. IEEE Trans. Autom. Control. 67 (4) (2022)
Urban Fasel, Nicola Fonzi, Andrea Iannelli, Steven L. Brunton
FlexWing-ROM: A matlab framework for data-driven reduced-order modeling of flexible wings. J. Open Source Softw. 7 (80) (2022)
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. Kutz, Steven L. Brunton
PySINDy: A comprehensive Python package for robust sparse system identification. J. Open Source Softw. 7 (69) (2022)
Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos
-body computations. J. Comput. Phys. 459 (2022)
Emma Hansen, Steven L. Brunton, Zhuoyuan Song
Swarm Modeling With Dynamic Mode Decomposition. IEEE Access 10 (2022)
Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J. Nathan Kutz, Steven L. Brunton, YuFeng Chen, Jonathan P. How
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC. CoRR (2022)
Lauren Conger, Jing Shuang Lisa Li, Eric Mazumdar, Steven L. Brunton
Nonlinear System Level Synthesis for Polynomial Dynamical Systems. CDC (2022)
Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz
Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data. Mach. Learn. Sci. Technol. 3 (1) (2022)
Shaowu Pan, Steven L. Brunton, J. Nathan Kutz
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. CoRR (2022)
Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz
Dimensionally consistent learning with Buckingham Pi. Nat. Comput. Sci. 2 (12) (2022)
Ricardo Vinuesa, Steven L. Brunton
Emerging trends in machine learning for computational fluid dynamics. CoRR (2022)
Andy Goldschmidt, Jonathan L. Dubois, Steven L. Brunton, J. Nathan Kutz
Model predictive control for robust quantum state preparation. Quantum 6 (2022)
Said Ouala, Steven L. Brunton, Ananda Pascual, Bertrand Chapron, Fabrice Collard, Lucile Gaultier, Ronan Fablet
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning. CoRR (2022)
Kadierdan Kaheman, Urban Fasel, Jason J. Bramburger, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton
The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control. CoRR (2022)
Ricardo Vinuesa, Steven L. Brunton
Enhancing computational fluid dynamics with machine learning. Nat. Comput. Sci. 2 (6) (2022)
Urban Fasel, Eurika Kaiser, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton
SINDy with Control: A Tutorial. CDC (2021)
Manu Kalia, Steven L. Brunton, Hil G. E. Meijer, Christoph Brune, J. Nathan Kutz
Learning normal form autoencoders for data-driven discovery of universal, parameter-dependent governing equations. CoRR (2021)
Steven N. Rodriguez, Athanasios P. Iliopoulos, Kevin T. Carlberg, Steven L. Brunton, John C. Steuben, John G. Michopoulos
Projection-tree reduced order modeling for fast N-body computations. CoRR (2021)
Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton
Data-driven discovery of Koopman eigenfunctions for control. Mach. Learn. Sci. Technol. 2 (3) (2021)
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
PySINDy: A comprehensive Python package for robust sparse system identification. CoRR (2021)
Kartik Krishna, Zhuoyuan Song, Steven L. Brunton
Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows. CoRR (2021)
Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton
PySensors: A Python package for sparse sensor placement. J. Open Source Softw. 6 (58) (2021)
Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz
Modern Koopman Theory for Dynamical Systems. CoRR (2021)
Craig R. Gin, Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz
DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems. CoRR (2021)
Steven L. Brunton
Applying Machine Learning to Study Fluid Mechanics. CoRR (2021)
Urban Fasel, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control. CoRR (2021)
Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz
Extraction of Instantaneous Frequencies and Amplitudes in Nonstationary Time-Series Data. IEEE Access 9 (2021)
Brian M. de Silva, Krithika Manohar, Emily Clark, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz
PySensors: A Python Package for Sparse Sensor Placement. CoRR (2021)
Jason J. Bramburger, J. Nathan Kutz, Steven L. Brunton
Data-Driven Stabilization of Periodic Orbits. IEEE Access 9 (2021)
Ricardo Vinuesa, Steven L. Brunton
The Potential of Machine Learning to Enhance Computational Fluid Dynamics. CoRR (2021)
Peter J. Baddoo, Benjamin Herrmann, Beverley J. McKeon, J. Nathan Kutz, Steven L. Brunton
Physics-informed dynamic mode decomposition (piDMD). CoRR (2021)
Henning Lange, Steven L. Brunton, J. Nathan Kutz
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. J. Mach. Learn. Res. 22 (2021)
Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz
Deep Learning of Conjugate Mappings. CoRR (2021)
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
Deeptime: a Python library for machine learning dynamical models from time series data. CoRR (2021)
Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger, Steven L. Brunton, J. Nathan Kutz
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data. CoRR (2021)
Kadierdan Kaheman, J. Nathan Kutz, Steven L. Brunton
SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics. CoRR (2020)
Brian de Silva, Kathleen P. Champion, Markus Quade, Jean-Christophe Loiseau, J. Nathan Kutz, Steven L. Brunton
PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data. J. Open Source Softw. 5 (49) (2020)
Emily Clark, Angelie Vincent, J. Nathan Kutz, Steven L. Brunton
Bracketing brackets with bras and kets. CoRR (2020)
N. Benjamin Erichson, Peng Zheng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
Sparse Principal Component Analysis via Variable Projection. SIAM J. Appl. Math. 80 (2) (2020)
Daniel Dylewsky, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
Principal Component Trajectories (PCT): Nonlinear dynamics as a superposition of time-delayed periodic orbits. CoRR (2020)
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
Physics-informed machine learning for sensor fault detection with flight test data. CoRR (2020)
Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz
Discovery of Physics From Data: Universal Laws and Discrepancies. Frontiers Artif. Intell. 3 (2020)
Kathleen P. Champion, Peng Zheng, Aleksandr Y. Aravkin, Steven L. Brunton, J. Nathan Kutz
A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data. IEEE Access 8 (2020)
Mason Kamb, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
Time-Delay Observables for Koopman: Theory and Applications. SIAM J. Appl. Dyn. Syst. 19 (2) (2020)
Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz
Multiresolution Convolutional Autoencoders. CoRR (2020)
Yuying Liu, J. Nathan Kutz, Steven L. Brunton
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers. CoRR (2020)
Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz
SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems. CoRR (2020)
Henning Lange, Steven L. Brunton, J. Nathan Kutz
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. CoRR (2020)
Thomas L. Mohren, Thomas L. Daniel, Steven L. Brunton
Learning Precisely Timed Feedforward Control of the Sensor-Denied Inverted Pendulum. IEEE Control. Syst. Lett. 4 (3) (2020)
Chang Sun, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz
Deep reinforcement learning for optical systems: A case study of mode-locked lasers. Mach. Learn. Sci. Technol. 1 (4) (2020)
Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data. CoRR (2020)
N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz
Randomized CP tensor decomposition. Mach. Learn. Sci. Technol. 1 (2) (2020)
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
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning. CoRR (2020)
Kathleen P. Champion, Steven L. Brunton, J. Nathan Kutz
Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings. SIAM J. Appl. Dyn. Syst. 18 (1) (2019)
Kadierdan Kaheman, Eurika Kaiser, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton
Learning Discrepancy Models From Experimental Data. CoRR (2019)
N. Benjamin Erichson, Lionel Mathelin, Zhewei Yao, Steven L. Brunton, Michael W. Mahoney, J. Nathan Kutz
Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data. CoRR (2019)
Zhe Bai, N. Benjamin Erichson, Muralikrishnan Gopalakrishnan Meena, Kunihiko Taira, Steven L. Brunton
Randomized methods to characterize large-scale vortical flow network. CoRR (2019)
Samuel H. Rudy, Alessandro Alla, Steven L. Brunton, J. Nathan Kutz
Data-Driven Identification of Parametric Partial Differential Equations. SIAM J. Appl. Dyn. Syst. 18 (2) (2019)
Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz
Deep Model Predictive Control with Online Learning for Complex Physical Systems. CoRR (2019)
Steven L. Brunton, Bernd Noack, Petros Koumoutsakos
Machine Learning for Fluid Mechanics. CoRR (2019)
N. Benjamin Erichson, Lionel Mathelin, J. Nathan Kutz, Steven L. Brunton
Randomized Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 18 (4) (2019)

Steven L. Brunton.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Steven L. Brunton