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Steven L Brunton

Showing results (1-10 of 50) with videos related to

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Nature Computational Science|January 4, 2024
Enhancing computational fluid dynamics with machine learningRicardo Vinuesa, Steven L Brunton
Chaos (Woodbury, N.Y.)|April 8, 2010
Fast computation of finite-time Lyapunov exponent fields for unsteady flowsSteven L Brunton, Clarence W Rowley
Nature Computational Science|June 28, 2024
Promising directions of machine learning for partial differential equationsSteven L Brunton, J Nathan Kutz
Journal of the Royal Society, Interface|December 21, 2021
Challenges in dynamic mode decompositionZiyou Wu, Steven L Brunton, Shai Revzen
Proceedings. Mathematical, Physical, and Engineering Sciences|February 24, 2022
Finite-horizon, energy-efficient trajectories in unsteady flowsKartik Krishna, Zhuoyuan Song, Steven L Brunton
Royal Society Open Science|August 25, 2021
Sparse nonlinear models of chaotic electroconvectionYifei Guan, Steven L Brunton, Igor Novosselov
Physical Review. E|April 15, 2016
Lagrangian coherent structures and inertial particle dynamicsM Sudharsan, Steven L Brunton, James J Riley
Proceedings. Mathematical, Physical, and Engineering Sciences|November 20, 2020
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamicsKadierdan Kaheman, J Nathan Kutz, Steven L Brunton
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|June 20, 2022
Hierarchical deep learning of multiscale differential equation time-steppersYuying Liu, J Nathan Kutz, Steven L Brunton
Nature Communications|November 25, 2018
Deep learning for universal linear embeddings of nonlinear dynamicsBethany Lusch, J Nathan Kutz, Steven L Brunton
Pageof 5

Showing results (1-10 of 50) with videos related to

Sort By:
Pageof 5
Nature Computational Science|January 4, 2024
Enhancing computational fluid dynamics with machine learningRicardo Vinuesa, Steven L Brunton
Chaos (Woodbury, N.Y.)|April 8, 2010
Fast computation of finite-time Lyapunov exponent fields for unsteady flowsSteven L Brunton, Clarence W Rowley
Nature Computational Science|June 28, 2024
Promising directions of machine learning for partial differential equationsSteven L Brunton, J Nathan Kutz
Journal of the Royal Society, Interface|December 21, 2021
Challenges in dynamic mode decompositionZiyou Wu, Steven L Brunton, Shai Revzen
Proceedings. Mathematical, Physical, and Engineering Sciences|February 24, 2022
Finite-horizon, energy-efficient trajectories in unsteady flowsKartik Krishna, Zhuoyuan Song, Steven L Brunton
Royal Society Open Science|August 25, 2021
Sparse nonlinear models of chaotic electroconvectionYifei Guan, Steven L Brunton, Igor Novosselov
Physical Review. E|April 15, 2016
Lagrangian coherent structures and inertial particle dynamicsM Sudharsan, Steven L Brunton, James J Riley
Proceedings. Mathematical, Physical, and Engineering Sciences|November 20, 2020
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamicsKadierdan Kaheman, J Nathan Kutz, Steven L Brunton
Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences|June 20, 2022
Hierarchical deep learning of multiscale differential equation time-steppersYuying Liu, J Nathan Kutz, Steven L Brunton
Nature Communications|November 25, 2018
Deep learning for universal linear embeddings of nonlinear dynamicsBethany Lusch, J Nathan Kutz, Steven L Brunton
Pageof 5