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Items: 18

1.

Sparse identification of nonlinear dynamics for model predictive control in the low-data limit.

Kaiser E, Kutz JN, Brunton SL.

Proc Math Phys Eng Sci. 2018 Nov;474(2219):20180335. doi: 10.1098/rspa.2018.0335. Epub 2018 Nov 14.

2.

Deep learning for universal linear embeddings of nonlinear dynamics.

Lusch B, Kutz JN, Brunton SL.

Nat Commun. 2018 Nov 23;9(1):4950. doi: 10.1038/s41467-018-07210-0.

3.

Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data.

Au-Yeung WM, Reinhall PG, Bardy GH, Brunton SL.

PLoS One. 2018 Nov 14;13(11):e0207215. doi: 10.1371/journal.pone.0207215. eCollection 2018.

4.

Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data.

Mohren TL, Daniel TL, Brunton SL, Brunton BW.

Proc Natl Acad Sci U S A. 2018 Oct 16;115(42):10564-10569. doi: 10.1073/pnas.1808909115. Epub 2018 Sep 13.

5.

Networked-oscillator-based modeling and control of unsteady wake flows.

Nair AG, Brunton SL, Taira K.

Phys Rev E. 2018 Jun;97(6-1):063107. doi: 10.1103/PhysRevE.97.063107.

PMID:
30011576
6.

Sparse identification of nonlinear dynamics for rapid model recovery.

Quade M, Abel M, Nathan Kutz J, Brunton SL.

Chaos. 2018 Jun;28(6):063116. doi: 10.1063/1.5027470.

PMID:
29960401
7.

Model selection for dynamical systems via sparse regression and information criteria.

Mangan NM, Kutz JN, Brunton SL, Proctor JL.

Proc Math Phys Eng Sci. 2017 Aug;473(2204):20170009. doi: 10.1098/rspa.2017.0009. Epub 2017 Aug 30.

8.

Chaos as an intermittently forced linear system.

Brunton SL, Brunton BW, Proctor JL, Kaiser E, Kutz JN.

Nat Commun. 2017 May 30;8(1):19. doi: 10.1038/s41467-017-00030-8.

9.

Data-driven discovery of partial differential equations.

Rudy SH, Brunton SL, Proctor JL, Kutz JN.

Sci Adv. 2017 Apr 26;3(4):e1602614. doi: 10.1126/sciadv.1602614. eCollection 2017 Apr.

10.

Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion.

Kunert JM, Proctor JL, Brunton SL, Kutz JN.

PLoS Comput Biol. 2017 Jan 11;13(1):e1005303. doi: 10.1371/journal.pcbi.1005303. eCollection 2017 Jan.

11.

Lagrangian coherent structures and inertial particle dynamics.

Sudharsan M, Brunton SL, Riley JJ.

Phys Rev E. 2016 Mar;93(3):033108. doi: 10.1103/PhysRevE.93.033108. Epub 2016 Mar 9.

PMID:
27078448
12.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Brunton SL, Proctor JL, Kutz JN.

Proc Natl Acad Sci U S A. 2016 Apr 12;113(15):3932-7. doi: 10.1073/pnas.1517384113. Epub 2016 Mar 28.

13.

Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.

Brunton SL, Brunton BW, Proctor JL, Kutz JN.

PLoS One. 2016 Feb 26;11(2):e0150171. doi: 10.1371/journal.pone.0150171. eCollection 2016.

14.

Extremum-seeking control of the beam pattern of a reconfigurable holographic metamaterial antenna.

Johnson MC, Brunton SL, Kundtz NB, Kutz NJ.

J Opt Soc Am A Opt Image Sci Vis. 2016 Jan 1;33(1):59-68. doi: 10.1364/JOSAA.33.000059.

PMID:
26831586
15.

Nonlinear model reduction for dynamical systems using sparse sensor locations from learned libraries.

Sargsyan S, Brunton SL, Kutz JN.

Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Sep;92(3):033304. doi: 10.1103/PhysRevE.92.033304. Epub 2015 Sep 10.

PMID:
26465583
16.

Classification of birefringence in mode-locked fiber lasers using machine learning and sparse representation.

Fu X, Brunton SL, Nathan Kutz J.

Opt Express. 2014 Apr 7;22(7):8585-97. doi: 10.1364/OE.22.008585.

PMID:
24718230
17.

Fast computation of finite-time Lyapunov exponent fields for unsteady flows.

Brunton SL, Rowley CW.

Chaos. 2010 Mar;20(1):017503. doi: 10.1063/1.3270044.

PMID:
20370293
18.

Avascular necrosis not Charcot's.

Samarasinghe YP, Brunton SL, Feher MD.

Diabet Med. 2001 Oct;18(10):846-8.

PMID:
11678977

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