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2020 – today
- 2024
[j55]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): 48-53 (2024)- [j54]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): 5881 (2024) - [j53]Steven L. Brunton, J. Nathan Kutz:
Promising directions of machine learning for partial differential equations. Nat. Comput. Sci. 4(7): 483-494 (2024) - [j52]Jiazhong Mei, Steven L. Brunton, J. Nathan Kutz:
Mobile Sensor Path Planning for Kalman Filter Spatiotemporal Estimation. Sensors 24(12): 3727 (2024) - [j51]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: 1124-1138 (2024) - [c11]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: 2530-2534 - [i83]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 abs/2402.07463 (2024) - [i82]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 abs/2402.09234 (2024) - [i81]Preston Rozwood, Edward Mehrez, Ludger Paehler, Wen Sun, Steven L. Brunton:
Koopman-Assisted Reinforcement Learning. CoRR abs/2403.02290 (2024) - [i80]Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Brunton:
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning. CoRR abs/2403.09110 (2024) - [i79]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 abs/2405.04161 (2024) - [i78]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 abs/2405.20905 (2024) - [i77]Tanner D. Harms, Steven L. Brunton, Beverley J. McKeon:
Estimating Dynamic Flow Features in Groups of Tracked Objects. CoRR abs/2408.16190 (2024) - [i76]Esther Lagemann, Julia Roeb, Steven L. Brunton, Christian Lagemann:
A deep learning approach to wall-shear stress quantification: From numerical training to zero-shot experimental application. CoRR abs/2409.03933 (2024) - [i75]Doris Voina, Steven L. Brunton, J. Nathan Kutz:
Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems. CoRR abs/2410.02079 (2024) - [i74]Maksym Zhelyeznuyakov, Johannes E. Fröch, Shane Colburn, Steven L. Brunton, Arka Majumdar:
Computed tomography using meta-optics. CoRR abs/2411.08995 (2024) - 2023
- [j50]Kartik Krishna, Steven L. Brunton, Zhuoyuan Song:
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning. IEEE Access 11: 118916-118930 (2023) - [j49]Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz:
Multiresolution convolutional autoencoders. J. Comput. Phys. 474: 111801 (2023) - [j48]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: 41:1-41:60 (2023) - [j47]Ana Larrañaga, Steven L. Brunton, Javier Martínez, Sergio 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): 25012 (2023) - [c10]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: 1922-1939 - [c9]Jiazhong Mei, J. Nathan Kutz, Steven L. Brunton:
Observability-Based Energy Efficient Path Planning with Background Flow via Deep Reinforcement Learning. CDC 2023: 4364-4371 - [c8]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: 3383-3389 - [i73]Sebastian Peitz, Jan Stenner, Vikas Chidananda, Oliver Wallscheid, Steven L. Brunton, Kunihiko Taira:
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning. CoRR abs/2301.10737 (2023) - [i72]L. Mars Gao, Urban Fasel, Steven L. Brunton, J. Nathan Kutz:
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery. CoRR abs/2301.12649 (2023) - [i71]Alan A. Kaptanoglu, Lanyue Zhang, Zachary G. Nicolaou, Urban Fasel, Steven L. Brunton:
Benchmarking sparse system identification with low-dimensional chaos. CoRR abs/2302.10787 (2023) - [i70]Ricardo Vinuesa, Steven L. Brunton, Beverley J. McKeon:
The transformative potential of machine learning for experiments in fluid mechanics. CoRR abs/2303.15832 (2023) - [i69]Steven L. Brunton, J. Nathan Kutz:
Machine Learning for Partial Differential Equations. CoRR abs/2303.17078 (2023) - [i68]Kartik Krishna, Steven L. Brunton, Zhuoyuan Song:
Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning. CoRR abs/2304.03326 (2023) - [i67]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 abs/2306.12962 (2023) - [i66]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 abs/2306.13637 (2023) - [i65]Kartik Krishna, Aditya G. Nair, Anand Krishnan, Steven L. Brunton, Eurika Kaiser:
Control of Vortex Dynamics using Invariants. CoRR abs/2308.03920 (2023) - [i64]Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz, Steven L. Brunton:
Nonlinear parametric models of viscoelastic fluid flows. CoRR abs/2308.04405 (2023) - [i63]Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz:
Multi-fidelity reduced-order surrogate modeling. CoRR abs/2309.00325 (2023) - [i62]Mozes Jacobs, Bingni W. Brunton, Steven L. Brunton, J. Nathan Kutz, Ryan V. Raut:
HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations. CoRR abs/2310.04832 (2023) - [i61]Esther Lagemann, Steven L. Brunton, Christian Lagemann:
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements. CoRR abs/2310.11147 (2023) - [i60]Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton:
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning. CoRR abs/2311.00212 (2023) - 2022
- [j46]Emma Hansen, Steven L. Brunton, Zhuoyuan Song:
Swarm Modeling With Dynamic Mode Decomposition. IEEE Access 10: 59508-59521 (2022) - [j45]Ricardo Vinuesa, Steven L. Brunton:
Emerging Trends in Machine Learning for Computational Fluid Dynamics. Comput. Sci. Eng. 24(5): 33-41 (2022) - [j44]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. J. Comput. Phys. 459: 111141 (2022) - [j43]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:
PySINDy: A comprehensive Python package for robust sparse system identification. J. Open Source Softw. 7(69): 3994 (2022) - [j42]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): 4211 (2022) - [j41]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): 15009 (2022) - [j40]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): 15031 (2022) - [j39]Ricardo Vinuesa, Steven L. Brunton:
Enhancing computational fluid dynamics with machine learning. Nat. Comput. Sci. 2(6): 358-366 (2022) - [j38]Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz:
Dimensionally consistent learning with Buckingham Pi. Nat. Comput. Sci. 2(12): 834-844 (2022) - [j37]Andy Goldschmidt, Jonathan L. Dubois, Steven L. Brunton, J. Nathan Kutz:
Model predictive control for robust quantum state preparation. Quantum 6: 837 (2022) - [j36]Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz:
Modern Koopman Theory for Dynamical Systems. SIAM Rev. 64(2): 229-340 (2022) - [j35]Krithika Manohar, J. Nathan Kutz, Steven L. Brunton:
Optimal Sensor and Actuator Selection Using Balanced Model Reduction. IEEE Trans. Autom. Control. 67(4): 2108-2115 (2022) - [c7]Lauren E. Conger, Jing Shuang Lisa Li, Eric Mazumdar, Steven L. Brunton:
Nonlinear System Level Synthesis for Polynomial Dynamical Systems. CDC 2022: 3846-3852 - [i59]Joseph Bakarji, Kathleen P. Champion, J. Nathan Kutz, Steven L. Brunton:
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders. CoRR abs/2201.05136 (2022) - [i58]Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz:
Dimensionally Consistent Learning with Buckingham Pi. CoRR abs/2202.04643 (2022) - [i57]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 abs/2202.05750 (2022) - [i56]Shaowu Pan, Steven L. Brunton, J. Nathan Kutz:
Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data. CoRR abs/2204.03216 (2022) - [i55]Emma Hansen, Steven L. Brunton, Zhuoyuan Song:
Swarm Modelling with Dynamic Mode Decomposition. CoRR abs/2204.06335 (2022) - [i54]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 abs/2205.06231 (2022) - [i53]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 abs/2209.10007 (2022) - [i52]Ricardo Vinuesa, Steven L. Brunton:
Emerging trends in machine learning for computational fluid dynamics. CoRR abs/2211.15145 (2022) - 2021
- [j34]Jason J. Bramburger, J. Nathan Kutz, Steven L. Brunton:
Data-Driven Stabilization of Periodic Orbits. IEEE Access 9: 43504-43521 (2021) - [j33]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: 83453-83466 (2021) - [j32]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: 41:1-41:38 (2021) - [j31]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): 2828 (2021) - [j30]Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton:
Data-driven discovery of Koopman eigenfunctions for control. Mach. Learn. Sci. Technol. 2(3): 35023 (2021) - [c6]Urban Fasel, Eurika Kaiser, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton:
SINDy with Control: A Tutorial. CDC 2021: 16-21 - [i51]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 abs/2101.07206 (2021) - [i50]Steven L. Brunton, Marko Budisic, Eurika Kaiser, J. Nathan Kutz:
Modern Koopman Theory for Dynamical Systems. CoRR abs/2102.12086 (2021) - [i49]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 abs/2102.13476 (2021) - [i48]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 abs/2103.01983 (2021) - [i47]Kartik Krishna, Zhuoyuan Song, Steven L. Brunton:
Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows. CoRR abs/2103.10556 (2021) - [i46]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 abs/2104.01293 (2021) - [i45]Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz:
Deep Learning of Conjugate Mappings. CoRR abs/2104.01874 (2021) - [i44]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 abs/2106.05102 (2021) - [i43]Steven L. Brunton:
Applying Machine Learning to Study Fluid Mechanics. CoRR abs/2110.02083 (2021) - [i42]Ricardo Vinuesa, Steven L. Brunton:
The Potential of Machine Learning to Enhance Computational Fluid Dynamics. CoRR abs/2110.02085 (2021) - [i41]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 abs/2110.15013 (2021) - [i40]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 abs/2111.08481 (2021) - [i39]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 abs/2111.10992 (2021) - [i38]Peter J. Baddoo, Benjamin Herrmann, Beverley J. McKeon, J. Nathan Kutz, Steven L. Brunton:
Physics-informed dynamic mode decomposition (piDMD). CoRR abs/2112.04307 (2021) - 2020
- [j29]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: 169259-169271 (2020) - [j28]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): 731-736 (2020) - [j27]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: 25 (2020) - [j26]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): 2104 (2020) - [j25]N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz:
Randomized CP tensor decomposition. Mach. Learn. Sci. Technol. 1(2): 25012 (2020) - [j24]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): 45013 (2020) - [j23]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): 886-917 (2020) - [j22]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): 977-1002 (2020) - [i37]Henning Lange, Steven L. Brunton, J. Nathan Kutz:
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction. CoRR abs/2004.00574 (2020) - [i36]Kadierdan Kaheman, J. Nathan Kutz, Steven L. Brunton:
SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics. CoRR abs/2004.02322 (2020) - [i35]Yuying Liu, Colin Ponce, Steven L. Brunton, J. Nathan Kutz:
Multiresolution Convolutional Autoencoders. CoRR abs/2004.04946 (2020) - [i34]Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz:
SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems. CoRR abs/2005.10756 (2020) - [i33]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 abs/2005.14321 (2020) - [i32]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 abs/2006.13380 (2020) - [i31]Yuying Liu, J. Nathan Kutz, Steven L. Brunton:
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers. CoRR abs/2008.09768 (2020) - [i30]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 abs/2008.10740 (2020) - [i29]Emily Clark, Angelie Vincent, J. Nathan Kutz, Steven L. Brunton:
Bracketing brackets with bras and kets. CoRR abs/2008.12247 (2020) - [i28]Kadierdan Kaheman, Steven L. Brunton, J. Nathan Kutz:
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data. CoRR abs/2009.08810 (2020)
2010 – 2019
- 2019
- [j21]Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin:
A Unified Framework for Sparse Relaxed Regularized Regression: SR3. IEEE Access 7: 1404-1423 (2019) - [j20]Samuel H. Rudy, J. Nathan Kutz, Steven L. Brunton:
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints. J. Comput. Phys. 396: 483-506 (2019) - [j19]Samuel H. Rudy, Steven L. Brunton, J. Nathan Kutz:
Smoothing and parameter estimation by soft-adherence to governing equations. J. Comput. Phys. 398 (2019) - [j18]N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz:
Compressed dynamic mode decomposition for background modeling. J. Real Time Image Process. 16(5): 1479-1492 (2019) - [j17]Krithika Manohar, Eurika Kaiser, Steven L. Brunton, J. Nathan Kutz:
Optimized Sampling for Multiscale Dynamics. Multiscale Model. Simul. 17(1): 117-136 (2019) - [j16]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): 312-333 (2019) - [j15]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): 643-660 (2019) - [j14]N. Benjamin Erichson, Lionel Mathelin, J. Nathan Kutz, Steven L. Brunton:
Randomized Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 18(4): 1867-1891 (2019) - [j13]Chen Gong, N. Benjamin Erichson, John P. Kelly, Laura C. Trutoiu, Brian T. Schowengerdt, Steven L. Brunton, Eric J. Seibel:
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology. IEEE Trans. Medical Imaging 38(8): 1993-2004 (2019) - [i27]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 abs/1902.07358 (2019) - [i26]Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz:
Deep Model Predictive Control with Online Learning for Complex Physical Systems. CoRR abs/1905.10094 (2019) - [i25]Steven L. Brunton, Bernd Noack, Petros Koumoutsakos:
Machine Learning for Fluid Mechanics. CoRR abs/1905.11075 (2019) - [i24]Brian de Silva, David M. Higdon, Steven L. Brunton, J. Nathan Kutz:
Discovery of Physics from Data: Universal Laws and Discrepancy Models. CoRR abs/1906.07906 (2019) - [i23]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. CoRR abs/1906.10612 (2019) - [i22]Zhe Bai, N. Benjamin Erichson, Muralikrishnan Gopalakrishnan Meena, Kunihiko Taira, Steven L. Brunton:
Randomized methods to characterize large-scale vortical flow network. CoRR abs/1909.00535 (2019) - [i21]Kadierdan Kaheman, Eurika Kaiser, Benjamin Strom, J. Nathan Kutz, Steven L. Brunton:
Learning Discrepancy Models From Experimental Data. CoRR abs/1909.08574 (2019) - [i20]Craig Gin, Bethany Lusch, Steven L. Brunton, J. Nathan Kutz:
Deep Learning Models for Global Coordinate Transformations that Linearize PDEs. CoRR abs/1911.02710 (2019) - [i19]Thomas L. Mohren, Thomas L. Daniel, Steven L. Brunton:
Learning Precisely Timed Feedforward Control of the Sensor-Denied Inverted Pendulum. CoRR abs/1912.04922 (2019) - 2018
- [j12]J. Nathan Kutz, Joshua L. Proctor, Steven L. Brunton:
Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems. Complex. 2018: 6010634:1-6010634:16 (2018) - [j11]Eurika Kaiser, Marek Morzynski, Guillaume Daviller, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton:
Sparsity enabled cluster reduced-order models for control. J. Comput. Phys. 352: 388-409 (2018) - [j10]Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz:
Generalizing Koopman Theory to Allow for Inputs and Control. SIAM J. Appl. Dyn. Syst. 17(1): 909-930 (2018) - [j9]Syuzanna Sargsyan, Steven L. Brunton, J. Nathan Kutz:
Online Interpolation Point Refinement for Reduced-Order Models using a Genetic Algorithm. SIAM J. Sci. Comput. 40(1) (2018) - [j8]Wei Guo, Krithika Manohar, Steven L. Brunton, Ashis Gopal Banerjee:
Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification. IEEE Trans. Knowl. Data Eng. 30(7): 1403-1408 (2018) - [c5]Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton:
Discovering Conservation Laws from Data for Control. CDC 2018: 6415-6421 - [i18]N. Benjamin Erichson, Lionel Mathelin, Steven L. Brunton, J. Nathan Kutz:
Diffusion Maps meet Nyström. CoRR abs/1802.08762 (2018) - [i17]N. Benjamin Erichson, Peng Zeng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin:
Sparse Principal Component Analysis via Variable Projection. CoRR abs/1804.00341 (2018) - [i16]Peng Zheng, Travis Askham, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin:
Sparse Relaxed Regularized Regression: SR3. CoRR abs/1807.05411 (2018) - [i15]Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton:
Discovering conservation laws from data for control. CoRR abs/1811.00961 (2018) - [i14]Chen Gong, N. Benjamin Erichson, John P. Kelly, Laura C. Trutoiu, Brian T. Schowengerdt, Steven L. Brunton, Eric J. Seibel:
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology. CoRR abs/1811.11874 (2018) - [i13]Krithika Manohar, J. Nathan Kutz, Steven L. Brunton:
Optimal Sensor and Actuator Placement using Balanced Model Reduction. CoRR abs/1812.01574 (2018) - 2017
- [j7]James M. Kunert, Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz:
Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion. PLoS Comput. Biol. 13(1) (2017) - [c4]J. Nathan Kutz, Samuel H. Rudy, Alessandro Alla, Steven L. Brunton:
Data-Driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology. CAMSAP 2017: 1-5 - [c3]Seth D. Pendergrass, Steven L. Brunton, J. Nathan Kutz, N. Benjamin Erichson, Travis Askham:
Dynamic Mode Decomposition for Background Modeling. ICCV Workshops 2017: 1862-1870 - [c2]N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz:
Compressed Singular Value Decomposition for Image and Video Processing. ICCV Workshops 2017: 1880-1888 - [i12]Wei Guo, Krithika Manohar, Steven L. Brunton, Ashis Gopal Banerjee:
Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification. CoRR abs/1701.03212 (2017) - [i11]Krithika Manohar, Bingni W. Brunton, J. Nathan Kutz, Steven L. Brunton:
Data-Driven Sparse Sensor Placement. CoRR abs/1701.07569 (2017) - [i10]N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz:
Randomized Dynamic Mode Decomposition. CoRR abs/1702.02912 (2017) - [i9]N. Benjamin Erichson, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz:
Randomized CP Tensor Decomposition. CoRR abs/1703.09074 (2017) - [i8]Zhe Bai, Eurika Kaiser, Joshua L. Proctor, J. Nathan Kutz, Steven L. Brunton:
Dynamic mode decomposition for compressive system identification. CoRR abs/1710.07737 (2017) - [i7]Thomas Baumeister, Steven L. Brunton, J. Nathan Kutz:
Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers. CoRR abs/1711.02702 (2017) - [i6]Bethany Lusch, J. Nathan Kutz, Steven L. Brunton:
Deep learning for universal linear embeddings of nonlinear dynamics. CoRR abs/1712.09707 (2017) - 2016
- [b1]J. Nathan Kutz, Steven L. Brunton, Bingni W. Brunton, Joshua L. Proctor:
Dynamic mode decomposition - data-driven modeling of complex systems. SIAM 2016, ISBN 978-1-611-97449-2, pp. 1-234 - [j6]Joshua L. Proctor, Steven L. Brunton, J. Nathan Kutz:
Dynamic Mode Decomposition with Control. SIAM J. Appl. Dyn. Syst. 15(1): 142-161 (2016) - [j5]J. Nathan Kutz, Xing Fu, Steven L. Brunton:
Multiresolution Dynamic Mode Decomposition. SIAM J. Appl. Dyn. Syst. 15(2): 713-735 (2016) - [j4]Bingni W. Brunton, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz:
Sparse Sensor Placement Optimization for Classification. SIAM J. Appl. Math. 76(5): 2099-2122 (2016) - [j3]Niall M. Mangan, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz:
Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics. IEEE Trans. Mol. Biol. Multi Scale Commun. 2(1): 52-63 (2016) - [i5]Syuzanna Sargsyan, Steven L. Brunton, J. Nathan Kutz:
Online interpolation point refinement for reduced order models using a genetic algorithm. CoRR abs/1607.07702 (2016) - [i4]N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz:
Randomized Matrix Decompositions using R. CoRR abs/1608.02148 (2016) - [i3]Seth D. Pendergrass, J. Nathan Kutz, Steven L. Brunton:
Streaming GPU Singular Value and Dynamic Mode Decompositions. CoRR abs/1612.07875 (2016) - 2015
- [c1]J. Nathan Kutz, Xing Fu, Steven L. Brunton, N. Benjamin Erichson:
Multi-resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking. ICCV Workshops 2015: 921-929 - [i2]N. Benjamin Erichson, Steven L. Brunton, J. Nathan Kutz:
Compressed Dynamic Mode Decomposition for Real-Time Object Detection. CoRR abs/1512.04205 (2015) - 2014
- [j2]Dirk M. Luchtenburg, Steven L. Brunton, Clarence W. Rowley:
Long-time uncertainty propagation using generalized polynomial chaos and flow map composition. J. Comput. Phys. 274: 783-802 (2014) - [j1]Steven L. Brunton, Jonathan H. Tu, Ido Bright, J. Nathan Kutz:
Compressive Sensing and Low-Rank Libraries for Classification of Bifurcation Regimes in Nonlinear Dynamical Systems. SIAM J. Appl. Dyn. Syst. 13(4): 1716-1732 (2014) - 2013
- [i1]Bingni W. Brunton, Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz:
Optimal Sensor Placement and Enhanced Sparsity for Classification. CoRR abs/1310.4217 (2013)
Coauthor Index
[j55] [j54] [j53] [j52] [j51] [c11] [i83] [i82] [i80] [i78] [i75] [j49] [j48] [c9] [c8] [i72] [i69] [i67] [i64] [i63] [i62] [i60] [j43] [j41] [j40] [j38] [j37] [j36] [j35] [i59] [i58] [i56] [i54] [i53] [j34] [j33] [j32] [j31] [j30] [c6] [i51] [i50] [i49] [i46] [i45] [i44] [i41] [i40] [i39] [i38] [j29] [j27] [j26] [j25] [j24] [j23] [j22] [i37] [i36] [i35] [i34] [i33] [i32] [i31] [i30] [i29] [i28] [j21] [j20] [j19] [j18] [j17] [j16] [j15] [j14] [i27] [i26] [i24] [i23] [i21] [i20] [j12] [j11] [j10] [j9] [c5] [i18] [i17] [i16] [i15] [i13] [j7] [c4] [c3] [c2] [i11] [i10] [i9] [i8] [i7] [i6] [b1] [j6] [j5] [j4] [j3] [i5] [i4] [i3] [c1] [i2] [j1] [i1]
Brian de Silva
aka: Brian M. de Silva
aka: Brian M. de Silva
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