| Year |
Citation |
Score |
| 2020 |
Erichson NB, Mathelin L, Yao Z, Brunton SL, Mahoney MW, Kutz JN. Shallow neural networks for fluid flow reconstruction with limited sensors. Proceedings. Mathematical, Physical, and Engineering Sciences. 476: 20200097. PMID 32831593 DOI: 10.1098/Rspa.2020.0097 |
0.42 |
|
| 2020 |
Taira K, Hemati MS, Brunton SL, Sun Y, Duraisamy K, Bagheri S, Dawson STM, Yeh CA. Modal Analysis of Fluid Flows: Applications and Outlook Aiaa Journal. 58: 998-1022. DOI: 10.2514/1.J058462 |
0.754 |
|
| 2020 |
Bai Z, Kaiser E, Proctor JL, Kutz JN, Brunton SL. Dynamic Mode Decomposition for Compressive System Identification Aiaa Journal. 58: 561-574. DOI: 10.2514/1.J057870 |
0.358 |
|
| 2020 |
Brunton SL, Noack BR, Koumoutsakos P. Machine Learning for Fluid Mechanics Annual Review of Fluid Mechanics. 52: 477-508. DOI: 10.1146/Annurev-Fluid-010719-060214 |
0.389 |
|
| 2020 |
Erichson NB, Zheng P, Manohar K, Brunton SL, Kutz JN, Aravkin AY. Sparse Principal Component Analysis via Variable Projection Siam Journal On Applied Mathematics. 80: 977-1002. DOI: 10.1137/18M1211350 |
0.637 |
|
| 2020 |
Champion KP, Zheng P, Aravkin AY, Brunton SL, Kutz JN. A unified sparse optimization framework to learn parsimonious physics-informed models from data Ieee Access. 1-1. DOI: 10.1109/Access.2020.3023625 |
0.302 |
|
| 2020 |
Nair AG, Strom B, Brunton BW, Brunton SL. Phase-consistent dynamic mode decomposition from multiple overlapping spatial domains Physical Review Fluids. 5. DOI: 10.1103/Physrevfluids.5.074702 |
0.367 |
|
| 2020 |
Erichson NB, Mathelin L, Yao Z, Brunton SL, Mahoney MW, Kutz JN. Shallow neural networks for fluid flow reconstruction with limited sensors Proceedings of the Royal Society a: Mathematical, Physical and Engineering Sciences. 476: 20200097. DOI: 10.1098/rspa.2020.0097 |
0.309 |
|
| 2020 |
Fonzi N, Brunton SL, Fasel U. Data-driven nonlinear aeroelastic models of morphing wings for control Proceedings of the Royal Society a: Mathematical, Physical and Engineering Sciences. 476: 20200079. DOI: 10.1098/Rspa.2020.0079 |
0.4 |
|
| 2020 |
Kaptanoglu AA, Morgan KD, Hansen CJ, Brunton SL. Characterizing magnetized plasmas with dynamic mode decomposition Physics of Plasmas. 27: 032108. DOI: 10.1063/1.5138932 |
0.341 |
|
| 2020 |
Brunton SL, Hemati MS, Taira K. Special issue on machine learning and data-driven methods in fluid dynamics Theoretical and Computational Fluid Dynamics. 34: 1-5. DOI: 10.1007/S00162-020-00542-Y |
0.748 |
|
| 2020 |
Mendible A, Brunton SL, Aravkin AY, Lowrie W, Kutz JN. Dimensionality reduction and reduced-order modeling for traveling wave physics Theoretical and Computational Fluid Dynamics. 34: 385-400. DOI: 10.1007/S00162-020-00529-9 |
0.377 |
|
| 2020 |
Bieker K, Peitz S, Brunton SL, Kutz JN, Dellnitz M. Deep model predictive flow control with limited sensor data and online learning Theoretical and Computational Fluid Dynamics. 34: 577-591. DOI: 10.1007/S00162-020-00520-4 |
0.43 |
|
| 2019 |
Bai Z, Erichson NB, Gopalakrishnan Meena M, Taira K, Brunton SL. Randomized methods to characterize large-scale vortical flow networks. Plos One. 14: e0225265. PMID 31738778 DOI: 10.1371/Journal.Pone.0225265 |
0.568 |
|
| 2019 |
Champion K, Lusch B, Kutz JN, Brunton SL. Data-driven discovery of coordinates and governing equations. Proceedings of the National Academy of Sciences of the United States of America. 116: 22445-22451. PMID 31636218 DOI: 10.1073/Pnas.1906995116 |
0.392 |
|
| 2019 |
Li S, Kaiser E, Laima S, Li H, Brunton SL, Kutz JN. Discovering time-varying aerodynamics of a prototype bridge by sparse identification of nonlinear dynamical systems. Physical Review. E. 100: 022220. PMID 31574688 DOI: 10.1103/Physreve.100.022220 |
0.305 |
|
| 2019 |
Mangan NM, Askham T, Brunton SL, Kutz JN, Proctor JL. Model selection for hybrid dynamical systems via sparse regression. Proceedings. Mathematical, Physical, and Engineering Sciences. 475: 20180534. PMID 31007544 DOI: 10.1098/Rspa.2018.0534 |
0.37 |
|
| 2019 |
Erichson NB, Voronin S, Brunton SL, Kutz JN. Randomized Matrix Decompositions Using R Journal of Statistical Software. 89. DOI: 10.18637/Jss.V089.I11 |
0.313 |
|
| 2019 |
Erichson NB, Mathelin L, Kutz JN, Brunton SL. Randomized Dynamic Mode Decomposition Siam Journal On Applied Dynamical Systems. 18: 1867-1891. DOI: 10.1137/18M1215013 |
0.33 |
|
| 2019 |
Champion KP, Brunton SL, Kutz JN. Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings Siam Journal On Applied Dynamical Systems. 18: 312-333. DOI: 10.1137/18M1188227 |
0.335 |
|
| 2019 |
Manohar K, Kaiser E, Brunton SL, Kutz JN. Optimized Sampling for Multiscale Dynamics Multiscale Modeling & Simulation. 17: 117-136. DOI: 10.1137/17M1162366 |
0.664 |
|
| 2019 |
Zheng P, Askham T, Brunton SL, Kutz JN, Aravkin AY. A Unified Framework for Sparse Relaxed Regularized Regression: SR3 Ieee Access. 7: 1404-1423. DOI: 10.1109/Access.2018.2886528 |
0.343 |
|
| 2019 |
Callaham JL, Maeda K, Brunton SL. Robust flow reconstruction from limited measurements via sparse representation Physical Review Fluids. 4. DOI: 10.1103/Physrevfluids.4.103907 |
0.38 |
|
| 2019 |
Nair AG, Yeh C, Kaiser E, Noack BR, Brunton SL, Taira K. Cluster-based feedback control of turbulent post-stall separated flows Journal of Fluid Mechanics. 875: 345-375. DOI: 10.1017/Jfm.2019.469 |
0.569 |
|
| 2019 |
Rudy SH, Brunton SL, Kutz JN. Smoothing and parameter estimation by soft-adherence to governing equations Journal of Computational Physics. 398: 108860. DOI: 10.1016/J.Jcp.2019.108860 |
0.384 |
|
| 2019 |
Rudy SH, Nathan Kutz J, Brunton SL. Deep learning of dynamics and signal-noise decomposition with time-stepping constraints Journal of Computational Physics. 396: 483-506. DOI: 10.1016/J.Jcp.2019.06.056 |
0.375 |
|
| 2019 |
Gupta S, Malte P, Brunton SL, Novosselov I. Prevention of lean flame blowout using a predictive chemical reactor network control Fuel. 236: 583-588. DOI: 10.1016/J.Fuel.2018.09.044 |
0.314 |
|
| 2018 |
Kaiser E, Kutz JN, Brunton SL. Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings. Mathematical, Physical, and Engineering Sciences. 474: 20180335. PMID 30839858 DOI: 10.1098/Rspa.2018.0335 |
0.393 |
|
| 2018 |
Lusch B, Kutz JN, Brunton SL. Deep learning for universal linear embeddings of nonlinear dynamics. Nature Communications. 9: 4950. PMID 30470743 DOI: 10.1038/S41467-018-07210-0 |
0.384 |
|
| 2018 |
Mohren TL, Daniel TL, Brunton SL, Brunton BW. Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data. Proceedings of the National Academy of Sciences of the United States of America. PMID 30213850 DOI: 10.1073/Pnas.1808909115 |
0.331 |
|
| 2018 |
Nair AG, Brunton SL, Taira K. Networked-oscillator-based modeling and control of unsteady wake flows. Physical Review. E. 97: 063107. PMID 30011576 DOI: 10.1103/Physreve.97.063107 |
0.571 |
|
| 2018 |
Quade M, Abel M, Nathan Kutz J, Brunton SL. Sparse identification of nonlinear dynamics for rapid model recovery. Chaos (Woodbury, N.Y.). 28: 063116. PMID 29960401 DOI: 10.1063/1.5027470 |
0.371 |
|
| 2018 |
Taira K, Brunton SL, Dawson STM, Rowley CW, Colonius T, McKeon BJ, Schmidt OT, Gordeyev S, Theofilis V, Ukeiley LS. Correction: Modal Analysis of Fluid Flows: An Overview Aiaa Journal. 0-0. DOI: 10.2514/1.J056060.C1 |
0.729 |
|
| 2018 |
Baumeister T, Brunton SL, Nathan Kutz J. Deep learning and model predictive control for self-tuning mode-locked lasers Journal of the Optical Society of America B. 35: 617. DOI: 10.1364/Josab.35.000617 |
0.317 |
|
| 2018 |
Nathan Kutz J, Proctor JL, Brunton SL. Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems Complexity. 2018: 1-16. DOI: 10.1155/2018/6010634 |
0.351 |
|
| 2018 |
Sargsyan S, Brunton SL, Kutz JN. Online Interpolation Point Refinement for Reduced-Order Models using a Genetic Algorithm Siam Journal On Scientific Computing. 40: B283-B304. DOI: 10.1137/16M1086352 |
0.342 |
|
| 2018 |
Proctor JL, Brunton SL, Kutz JN. Generalizing Koopman Theory to Allow for Inputs and Control Siam Journal On Applied Dynamical Systems. 17: 909-930. DOI: 10.1137/16M1062296 |
0.32 |
|
| 2018 |
Brunton S. Data-driven discovery of dynamics for control The Journal of the Acoustical Society of America. 144: 1743-1743. DOI: 10.1121/1.5067728 |
0.376 |
|
| 2018 |
Guo W, Manohar K, Brunton SL, Banerjee AG. Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification Ieee Transactions On Knowledge and Data Engineering. 30: 1403-1408. DOI: 10.1109/Tkde.2018.2790386 |
0.654 |
|
| 2018 |
Manohar K, Brunton BW, Kutz JN, Brunton SL. Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns Ieee Control Systems Magazine. 38: 63-86. DOI: 10.1109/Mcs.2018.2810460 |
0.636 |
|
| 2018 |
Loiseau J, Noack BR, Brunton SL. Sparse reduced-order modelling: sensor-based dynamics to full-state estimation Journal of Fluid Mechanics. 844: 459-490. DOI: 10.1017/Jfm.2018.147 |
0.407 |
|
| 2018 |
Loiseau J, Brunton SL. Constrained sparse Galerkin regression Journal of Fluid Mechanics. 838: 42-67. DOI: 10.1017/Jfm.2017.823 |
0.409 |
|
| 2018 |
Manohar K, Hogan T, Buttrick J, Banerjee AG, Kutz JN, Brunton SL. Predicting shim gaps in aircraft assembly with machine learning and sparse sensing Journal of Manufacturing Systems. 48: 87-95. DOI: 10.1016/J.Jmsy.2018.01.011 |
0.672 |
|
| 2018 |
Kaiser E, Morzyński M, Daviller G, Kutz JN, Brunton BW, Brunton SL. Sparsity enabled cluster reduced-order models for control Journal of Computational Physics. 352: 388-409. DOI: 10.1016/J.Jcp.2017.09.057 |
0.441 |
|
| 2017 |
Mangan NM, Kutz JN, Brunton SL, Proctor JL. Model selection for dynamical systems via sparse regression and information criteria. Proceedings. Mathematical, Physical, and Engineering Sciences. 473: 20170009. PMID 28878554 DOI: 10.1098/Rspa.2017.0009 |
0.336 |
|
| 2017 |
Brunton SL, Brunton BW, Proctor JL, Kaiser E, Kutz JN. Chaos as an intermittently forced linear system. Nature Communications. 8: 19. PMID 28559566 DOI: 10.1038/S41467-017-00030-8 |
0.354 |
|
| 2017 |
Rudy SH, Brunton SL, Proctor JL, Kutz JN. Data-driven discovery of partial differential equations. Science Advances. 3: e1602614. PMID 28508044 DOI: 10.1126/Sciadv.1602614 |
0.341 |
|
| 2017 |
Kunert JM, Proctor JL, Brunton SL, Kutz JN. Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion. Plos Computational Biology. 13: e1005303. PMID 28076347 DOI: 10.1371/Journal.Pcbi.1005303 |
0.346 |
|
| 2017 |
Taira K, Brunton SL, Dawson STM, Rowley CW, Colonius T, McKeon BJ, Schmidt OT, Gordeyev S, Theofilis V, Ukeiley LS. Modal Analysis of Fluid Flows: An Overview Aiaa Journal. 55: 4013-4041. DOI: 10.2514/1.J056060 |
0.755 |
|
| 2017 |
Strom B, Brunton SL, Polagye B. Intracycle angular velocity control of cross-flow turbines Nature Energy. 2. DOI: 10.1038/Nenergy.2017.103 |
0.343 |
|
| 2017 |
Manohar K, Brunton SL, Kutz JN. Environment identification in flight using sparse approximation of wing strain Journal of Fluids and Structures. 70: 162-180. DOI: 10.1016/J.Jfluidstructs.2017.01.008 |
0.649 |
|
| 2016 |
Sudharsan M, Brunton SL, Riley JJ. Lagrangian coherent structures and inertial particle dynamics. Physical Review. E. 93: 033108. PMID 27078448 DOI: 10.1103/Physreve.93.033108 |
0.3 |
|
| 2016 |
Brunton SL, Proctor JL, Kutz JN. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences of the United States of America. PMID 27035946 DOI: 10.1073/Pnas.1517384113 |
0.392 |
|
| 2016 |
Brunton SL, Brunton BW, Proctor JL, Kutz JN. Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control. Plos One. 11: e0150171. PMID 26919740 DOI: 10.1371/Journal.Pone.0150171 |
0.371 |
|
| 2016 |
Johnson MC, Brunton SL, Kundtz NB, Kutz NJ. Extremum-seeking control of the beam pattern of a reconfigurable holographic metamaterial antenna. Journal of the Optical Society of America. a, Optics, Image Science, and Vision. 33: 59-68. PMID 26831586 DOI: 10.1364/Josaa.33.000059 |
0.693 |
|
| 2016 |
Brunton SL, Proctor JL, Tu JH, Kutz JN. Compressed sensing and dynamic mode decomposition Acm Journal of Computer Documentation. 2: 165-191. DOI: 10.3934/Jcd.2015002 |
0.37 |
|
| 2016 |
Proctor JL, Brunton SL, Kutz JN. Including inputs and control within equation-free architectures for complex systems The European Physical Journal Special Topics. 225: 2413-2434. DOI: 10.1140/Epjst/E2016-60057-9 |
0.33 |
|
| 2016 |
Brunton BW, Brunton SL, Proctor JL, Kutz JN. Sparse Sensor Placement Optimization for Classification Siam Journal On Applied Mathematics. 76: 2099-2122. DOI: 10.1137/15M1036713 |
0.341 |
|
| 2016 |
Kutz JN, Fu X, Brunton SL. Multiresolution dynamic mode decomposition Siam Journal On Applied Dynamical Systems. 15: 713-735. DOI: 10.1137/15M1023543 |
0.391 |
|
| 2016 |
Proctor JL, Brunton SL, Kutz JN. Dynamic mode decomposition with control Siam Journal On Applied Dynamical Systems. 15: 142-161. DOI: 10.1137/15M1013857 |
0.41 |
|
| 2016 |
Mangan NM, Brunton SL, Proctor JL, Kutz JN. Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics Ieee Transactions On Molecular, Biological and Multi-Scale Communications. 2: 52-63. DOI: 10.1109/Tmbmc.2016.2633265 |
0.35 |
|
| 2016 |
Parezanović V, Cordier L, Spohn A, Duriez T, Noack BR, Bonnet JP, Segond M, Abel M, Brunton SL. Frequency selection by feedback control in a turbulent shear flow Journal of Fluid Mechanics. 247-283. DOI: 10.1017/Jfm.2016.261 |
0.355 |
|
| 2016 |
Taira K, Nair AG, Brunton SL. Network structure of two-dimensional decaying isotropic turbulence Journal of Fluid Mechanics. 795: 795R21-795R213. DOI: 10.1017/Jfm.2016.235 |
0.527 |
|
| 2016 |
Brunton SL, Proctor JL, Kutz JN. Sparse Identification of Nonlinear Dynamics with Control (SINDYc)**SLB acknowledges support from the U.S. Air Force Center of Excellence on Nature Inspired Flight Technologies and Ideas (FA9550-14-1-0398). JLP thanks Bill and Melinda Gates for their active support of the Institute of Disease Modeling and their sponsorship through the Global Good Fund. JNK acknowledges support from the U.S. Air Force Office of Scientific Research (FA9550-09-0174). Ifac-Papersonline. 49: 710-715. DOI: 10.1016/J.Ifacol.2016.10.249 |
0.391 |
|
| 2016 |
Erichson NB, Brunton SL, Kutz JN. Compressed dynamic mode decomposition for background modeling Journal of Real-Time Image Processing. 16: 1479-1492. DOI: 10.1007/S11554-016-0655-2 |
0.351 |
|
| 2015 |
Sargsyan S, Brunton SL, Kutz JN. Nonlinear model reduction for dynamical systems using sparse sensor locations from learned libraries. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 92: 033304. PMID 26465583 DOI: 10.1103/Physreve.92.033304 |
0.385 |
|
| 2015 |
Kutz JN, Brunton SL. Intelligent Systems for Stabilizing Mode-Locked Lasers and Frequency Combs: Machine Learning and Equation-Free Control Paradigms for Self-Tuning Optics Nanophotonics. 4: 459-471. DOI: 10.1515/Nanoph-2015-0024 |
0.324 |
|
| 2015 |
Brunton SL, Noack BR. Closed-loop turbulence control: Progress and challenges Applied Mechanics Reviews. 67. DOI: 10.1115/1.4031175 |
0.367 |
|
| 2015 |
Parezanović V, Laurentie JC, Fourment C, Delville J, Bonnet JP, Spohn A, Duriez T, Cordier L, Noack BR, Abel M, Segond M, Shaqarin T, Brunton SL. Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control Flow, Turbulence and Combustion. 94: 155-173. DOI: 10.1007/S10494-014-9581-1 |
0.346 |
|
| 2014 |
Tu JH, Rowley CW, Luchtenburg DM, Brunton SL, Kutz JN. On dynamic mode decomposition: Theory and applications Acm Journal of Computer Documentation. 1: 391-421. DOI: 10.3934/Jcd.2014.1.391 |
0.623 |
|
| 2014 |
Proctor JL, Brunton SL, Brunton BW, Kutz JN. Exploiting sparsity and equation-free architectures in complex systems European Physical Journal: Special Topics. 223: 2665-2684. DOI: 10.1140/Epjst/E2014-02285-8 |
0.381 |
|
| 2014 |
Brunton SL, Tu JH, Bright I, Kutz JN. Compressive sensing and low-rank libraries for classification of bifurcation regimes in nonlinear dynamical systems Siam Journal On Applied Dynamical Systems. 13: 1716-1732. DOI: 10.1137/130949282 |
0.395 |
|
| 2014 |
Brunton SL, Fu X, Kutz JN. Self-Tuning Fiber Lasers Ieee Journal On Selected Topics in Quantum Electronics. 20. DOI: 10.1117/12.2211773 |
0.317 |
|
| 2014 |
Brunton SL, Fu X, Kutz JN. Self-Tuning Fiber Lasers Ieee Journal of Selected Topics in Quantum Electronics. 20: 464-471. DOI: 10.1109/Jstqe.2014.2336538 |
0.316 |
|
| 2014 |
Brunton SL, Dawson STM, Rowley CW. State-space model identification and feedback control of unsteady aerodynamic forces Journal of Fluids and Structures. 50: 253-270. DOI: 10.1016/J.Jfluidstructs.2014.06.026 |
0.615 |
|
| 2014 |
Luchtenburg DM, Brunton SL, Rowley CW. Long-time uncertainty propagation using generalized polynomial chaos and flow map composition Journal of Computational Physics. 274: 783-802. DOI: 10.1016/J.Jcp.2014.06.029 |
0.6 |
|
| 2013 |
Brunton SL, Rowley CW, Williams DR. Reduced-order unsteady aerodynamic models at low Reynolds numbers Journal of Fluid Mechanics. 724: 203-233. DOI: 10.1017/Jfm.2013.163 |
0.623 |
|
| 2013 |
Brunton SL, Rowley CW. Empirical state-space representations for Theodorsen's lift model Journal of Fluids and Structures. 38: 174-186. DOI: 10.1016/J.Jfluidstructs.2012.10.005 |
0.59 |
|
| 2011 |
Brunton SL, Rowley CW, Williams DR. Linear unsteady aerodynamic models from wind tunnel measurements 41st Aiaa Fluid Dynamics Conference and Exhibit. |
0.519 |
|
| 2010 |
Brunton SL, Rowley CW. Fast computation of finite-time Lyapunov exponent fields for unsteady flows. Chaos (Woodbury, N.Y.). 20: 017503. PMID 20370293 DOI: 10.1063/1.3270044 |
0.593 |
|
| 2010 |
Brunton SL, Rowley CW, Kulkarni SR, Clarkson C. Maximum power point tracking for photovoltaic optimization using ripple-based extremum seeking control Ieee Transactions On Power Electronics. 25: 2531-2540. DOI: 10.1109/Tpel.2010.2049747 |
0.576 |
|
| 2010 |
Brunton SL, Rowley CW. Unsteady aerodynamic models for agile flight at low reynolds numbers 48th Aiaa Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. |
0.584 |
|
| 2009 |
Brunton SL, Rowley CW, Kulkarni SR, Clarkson C. Maximum power point tracking for photovoltaic optimization using extremum seeking Conference Record of the Ieee Photovoltaic Specialists Conference. 000013-000016. DOI: 10.1109/PVSC.2009.5411777 |
0.54 |
|
| 2009 |
Brunton SL, Rowley CW. Modeling the unsteady aerodynamic forces on small-scale wings 47th Aiaa Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. |
0.59 |
|
| 2008 |
Brunton SL, Rowley CW, Taira K, Colonius T, Collins J, Williams DR. Unsteady aerodynamic forces on small-scale wings: Experiments, simulations and models 46th Aiaa Aerospace Sciences Meeting and Exhibit. |
0.58 |
|
| Show low-probability matches. |