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Deep learning in fluid dynamics

Journal of fluid mechanics, 2017-03, Vol.814, p.1-4

Kutz, J. Nathan

2017

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Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

1, 2019

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Data-driven discovery of coordinates and governing equations

Proceedings of the National Academy of Sciences - PNAS, 2019-11, Vol.116 (45), p.22445-22451

2019

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Deep learning for universal linear embeddings of nonlinear dynamics

Nature communications, 2018-11, Vol.9 (1), p.4950-10, Article 4950

2018

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

Proceedings of the National Academy of Sciences - PNAS, 2016-04, Vol.113 (15), p.3932-3937

2016

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Chaos as an intermittently forced linear system

Nature communications, 2017-05, Vol.8 (1), p.19-19, Article 19

2017

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Deep learning of dynamics and signal-noise decomposition with time-stepping constraints

Journal of computational physics, 2019-11, Vol.396, p.483-506

2019

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DeepGreen: deep learning of Green's functions for nonlinear boundary value problems

Scientific reports, 2021-11, Vol.11 (1), p.21614-21614, Article 21614

2021

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Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

Journal of neuroscience methods, 2016-01, Vol.258, p.1-15

2016

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

PloS one, 2016-02, Vol.11 (2), p.e0150171-e0150171

2016

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Greedy Sensor Placement With Cost Constraints

IEEE sensors journal, 2019-04, Vol.19 (7), p.2642-2656

2019

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Forecasting dengue fever in Brazil: An assessment of climate conditions

PloS one, 2019-08, Vol.14 (8), p.e0220106-e0220106

2019

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Generalizing Koopman Theory to Allow for Inputs and Control

SIAM journal on applied dynamical systems, 2018, Vol.17 (1), p.909-930

2018

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Learning dominant physical processes with data-driven balance models

Nature communications, 2021-02, Vol.12 (1), p.1016-1016, Article 1016

2021

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Optimal Sensor and Actuator Selection Using Balanced Model Reduction

IEEE transactions on automatic control, 2022-04, Vol.67 (4), p.2108-2115

2022

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Computing spectra of linear operators using the Floquet–Fourier–Hill method

Journal of computational physics, 2006-11, Vol.219 (1), p.296-321

2006

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Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight

PLoS computational biology, 2022-09, Vol.18 (9), p.e1010512-e1010512

2022

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A Unified Framework for Sparse Relaxed Regularized Regression: SR3

IEEE access, 2019, Vol.7, p.1404-1423

2019

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Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems

Complexity (New York, N.Y.), 2018-01, Vol.2018, p.1-16

2018

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A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data

IEEE access, 2020, Vol.8, p.169259-169271

2020

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Mode-Locked Soliton Lasers

SIAM review, 2006-12, Vol.48 (4), p.629-678

Kutz, J. Nathan

2006

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Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data

Machine learning: science and technology, 2022-03, Vol.3 (1), p.15031

2022

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Deterministic generation of single soliton Kerr frequency comb in microresonators by a single shot pulsed trigger

Optics express, 2018-07, Vol.26 (14), p.18563-18577

2018

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Flower discrimination by pollinators in a dynamic chemical environment

Science (American Association for the Advancement of Science), 2014-06, Vol.344 (6191), p.1515-1518

2014

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Methods for data-driven multiscale model discovery for materials

JPhys materials, 2019-10, Vol.2 (4), p.44002

2019

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Deep learning in fluid dynamics

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

Data-driven discovery of coordinates and governing equations

Deep learning for universal linear embeddings of nonlinear dynamics

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

Chaos as an intermittently forced linear system

Deep learning of dynamics and signal-noise decomposition with time-stepping constraints

DeepGreen: deep learning of Green's functions for nonlinear boundary value problems

Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

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

Greedy Sensor Placement With Cost Constraints

Forecasting dengue fever in Brazil: An assessment of climate conditions

Generalizing Koopman Theory to Allow for Inputs and Control

Learning dominant physical processes with data-driven balance models

Optimal Sensor and Actuator Selection Using Balanced Model Reduction

Computing spectra of linear operators using the Floquet–Fourier–Hill method

Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight

A Unified Framework for Sparse Relaxed Regularized Regression: SR3

Applied Koopman Theory for Partial Differential Equations and Data-Driven Modeling of Spatio-Temporal Systems

A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data

Mode-Locked Soliton Lasers

Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data

Deterministic generation of single soliton Kerr frequency comb in microresonators by a single shot pulsed trigger

Flower discrimination by pollinators in a dynamic chemical environment

Methods for data-driven multiscale model discovery for materials

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