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Publications

A Unified Framework for Sparse Relaxed Regularized Regression: SR3

P. Zheng, T. Askham, S. L. Brunton, J. N. Kutz, and A. Y. Aravkin

IEEE Access, 7(1):1404--1423, 2019

Deep learning for universal linear embeddings of nonlinear dynamics

B. Lusch, J. N. Kutz, S. L. Brunton

Nature Communications, 9(1):4950, 2018

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

E. Kaiser, J. N. Kutz, and S. L. Brunton

Proceedings of the Royal Society A, 474(2219), 2018

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

T. Mohren, T. L. Daniel, S. L. Brunton, and B. W. Brunton

Proceedings of the National Academy of Sciences, 115(42):10564–10569, 2018

Predicting shim gaps in aircraft assembly with machine learning and sparse sensing

K. Manohar, T. Hogan, J. Buttrick, A. G. Banerjee, J. N. Kutz, and S. L. Brunton

Journal of Manufacturing Systems, 48(C):87-95, 2018

Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns

K. Manohar, B. W. Brunton, J. N. Kutz, and , S. L. Brunton

IEEE Control Systems Magazine, 38(3):63-86, 2018

Sparse reduced-order modeling: Sensor-based dynamics to full-state estimation

J. C. Loiseau, B. R. Noack, and S. L. Brunton

Journal of Fluid Mechanics, 844:459–490, 2018

Constrained sparse Galerkin regression

J. C. Loiseau and S. L. Brunton

Journal of Fluid Mechanics, 838:42–67, 2018

Modal Analysis of Fluid Flows: An Overview

K. Taira, S. L. Brunton, S. T. M. Dawson, C. W. Rowley, T. Colonius, B. J. McKeon, O. Schmidt, S. Gordeyev, V. Theofilis, and L. S. Ukeiley

AIAA Journal, 55(12):4013–4041, 2017

Intracycle angular velocity control of cross-flow turbines

B. Strom, S. L. Brunton, and B. Polagye

Nature Energy, 2(17103):1–9, 2017

Chaos as an intermittently forced linear system

S. L. Brunton, B. W. Brunton, J. L. Proctor, E. Kaiser, and J. N. Kutz

Nature Communications, 8(19):1–9, 2017

Data-driven discovery of partial differential equations

S. H. Rudy, S. L. Brunton, J. L. Proctor, and J. N. Kutz

Science Advances, 3:e1602614, 2017

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