Crossref Citations
This article has been cited by the following publications. This list is generated based on data provided by
Crossref.
Ibáñez, Rubén
Abisset-Chavanne, Emmanuelle
Ammar, Amine
González, David
Cueto, Elías
Huerta, Antonio
Duval, Jean Louis
and
Chinesta, Francisco
2018.
A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition.
Complexity,
Vol. 2018,
Issue. ,
p.
1.
Quade, Markus
Abel, Markus
Nathan Kutz, J.
and
Brunton, Steven L.
2018.
Sparse identification of nonlinear dynamics for rapid model recovery.
Chaos: An Interdisciplinary Journal of Nonlinear Science,
Vol. 28,
Issue. 6,
Lusch, Bethany
Kutz, J. Nathan
and
Brunton, Steven L.
2018.
Deep learning for universal linear embeddings of nonlinear dynamics.
Nature Communications,
Vol. 9,
Issue. 1,
Dale, Rick
and
Bhat, Harish S.
2018.
Equations of mind: Data science for inferring nonlinear dynamics of socio-cognitive systems.
Cognitive Systems Research,
Vol. 52,
Issue. ,
p.
275.
Nair, Aditya G.
Brunton, Steven L.
and
Taira, Kunihiko
2018.
Networked-oscillator-based modeling and control of unsteady wake flows.
Physical Review E,
Vol. 97,
Issue. 6,
Zhang, Sheng
and
Lin, Guang
2018.
Robust data-driven discovery of governing physical laws with error bars.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences,
Vol. 474,
Issue. 2217,
p.
20180305.
Kaiser, E.
Kutz, J. N.
and
Brunton, S. L.
2018.
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences,
Vol. 474,
Issue. 2219,
p.
20180335.
2018.
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns.
IEEE Control Systems,
Vol. 38,
Issue. 3,
p.
63.
Giuliani, Laura
and
Durand, Helen
2018.
Data-Based Nonlinear Model Identification in Economic Model Predictive Control.
Smart and Sustainable Manufacturing Systems,
Vol. 2,
Issue. 2,
p.
20180025.
Loiseau, Jean-Christophe
Noack, Bernd R.
and
Brunton, Steven L.
2018.
Sparse reduced-order modelling: sensor-based dynamics to full-state estimation.
Journal of Fluid Mechanics,
Vol. 844,
Issue. ,
p.
459.
Xie, X.
Mohebujjaman, M.
Rebholz, L. G.
and
Iliescu, T.
2018.
Data-Driven Filtered Reduced Order Modeling of Fluid Flows.
SIAM Journal on Scientific Computing,
Vol. 40,
Issue. 3,
p.
B834.
Mohebujjaman, M.
Rebholz, L.G.
and
Iliescu, T.
2019.
Physically constrained data‐driven correction for reduced‐order modeling of fluid flows.
International Journal for Numerical Methods in Fluids,
Vol. 89,
Issue. 3,
p.
103.
El Hasadi, Yousef M. F.
and
Padding, Johan T.
2019.
Solving fluid flow problems using semi-supervised symbolic regression on sparse data.
AIP Advances,
Vol. 9,
Issue. 11,
Mangan, N. M.
Askham, T.
Brunton, S. L.
Kutz, J. N.
and
Proctor, J. L.
2019.
Model selection for hybrid dynamical systems via sparse regression.
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences,
Vol. 475,
Issue. 2223,
p.
20180534.
Bengana, Y.
Loiseau, J.-Ch.
Robinet, J.-Ch.
and
Tuckerman, L. S.
2019.
Bifurcation analysis and frequency prediction in shear-driven cavity flow.
Journal of Fluid Mechanics,
Vol. 875,
Issue. ,
p.
725.
Zhang, Linan
and
Schaeffer, Hayden
2019.
On the Convergence of the SINDy Algorithm.
Multiscale Modeling & Simulation,
Vol. 17,
Issue. 3,
p.
948.
Montáns, Francisco J.
Chinesta, Francisco
Gómez-Bombarelli, Rafael
and
Kutz, J. Nathan
2019.
Data-driven modeling and learning in science and engineering
.
Comptes Rendus. Mécanique,
Vol. 347,
Issue. 11,
p.
845.
Callaham, Jared L.
Maeda, Kazuki
and
Brunton, Steven L.
2019.
Robust flow reconstruction from limited measurements via sparse representation.
Physical Review Fluids,
Vol. 4,
Issue. 10,
Bhadriraju, Bhavana
Narasingam, Abhinav
and
Kwon, Joseph Sang-Il
2019.
Machine learning-based adaptive model identification of systems: Application to a chemical process.
Chemical Engineering Research and Design,
Vol. 152,
Issue. ,
p.
372.
Rudy, Samuel H.
Nathan Kutz, J.
and
Brunton, Steven L.
2019.
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints.
Journal of Computational Physics,
Vol. 396,
Issue. ,
p.
483.