Articles with public access mandates - J. Nathan KutzLearn more
Not available anywhere: 12
Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates
N de Lacy, E McCauley, JN Kutz, VD Calhoun
Neuroimage 202, 116116, 2019
Mandates: US National Science Foundation, US National Institutes of Health
Modal analysis of turbulent flow near an inclined bank–longitudinal structure junction
N Heydari, P Diplas, J Nathan Kutz, S Sadeghi Eshkevari
Journal of Hydraulic Engineering 147 (3), 04020100, 2021
Mandates: US Department of Defense, US Department of Transportation
Saddle transport and chaos in the double pendulum
K Kaheman, JJ Bramburger, JN Kutz, SL Brunton
Nonlinear Dynamics 111 (8), 7199-7233, 2023
Mandates: US National Science Foundation, US Department of Defense
The access of Trichoderma reesei 6A to cellulose is blocked by isolated hemicelluloses and their derivatives in biomass hydrolysis
D Xin, M Yang, X Chen, J Zhang
RSC advances 6 (77), 73859-73868, 2016
Mandates: National Natural Science Foundation of China
Adaptive dimensionality-reduction for time-stepping in differential and partial differential equations
X Fu, JN Kutz
Numerical Mathematics: Theory, Methods and Applications 10 (4), 872-894, 2017
Mandates: US Department of Defense
Machine Learning Methods for Reduced Order Modeling
JN Kutz
Model Order Reduction and Applications: Cetraro, Italy 2021, 201-228, 2023
Mandates: US National Science Foundation
Deep learning for control of nonlinear optical systems
JN Kutz
AI and Optical Data Sciences II 11703, 63-70, 2021
Mandates: US Department of Defense
Instantaneous amplitude: Association of ventricular fibrillation waveform measures at time of shock with outcome in out-of-hospital cardiac arrest
X Jaureguibeitia, J Coult, D Sashidhar, J Blackwood, JN Kutz, ...
Journal of Electrocardiology 80, 11-16, 2023
Mandates: American Heart Association, Government of Spain
SINDy-PI
K Kaheman, JN Kutz, SL Brunton
Proceedings: Mathematical, Physical and Engineering Sciences 476 (2242), 1-25, 2020
Mandates: US Department of Defense
Shape constrained tensor decompositions
B Lusch, EC Chi, JN Kutz
2019 IEEE International Conference on Data Science and Advanced Analytics …, 2019
Mandates: US Department of Energy, US Department of Defense
Passive mode-locking using multi-mode fiber
E Ding, S Lefrançois, JN Kutz, FW Wise
Fiber Lasers VIII: Technology, Systems, and Applications 7914, 337-350, 2011
Mandates: US National Institutes of Health
Machine Learning Methods for Constructing Dynamic Models From Data
J Nathan Kutz
Machine Learning in Modeling and Simulation: Methods and Applications, 149-178, 2023
Mandates: US National Science Foundation
Available somewhere: 182
Data-driven discovery of partial differential equations
SH Rudy, SL Brunton, JL Proctor, JN Kutz
Science advances 3 (4), e1602614, 2017
Mandates: US Department of Defense
Deep learning for universal linear embeddings of nonlinear dynamics
B Lusch, JN Kutz, SL Brunton
Nature communications 9 (1), 4950, 2018
Mandates: US Department of Defense
Data-driven discovery of coordinates and governing equations
K Champion, B Lusch, JN Kutz, SL Brunton
Proceedings of the National Academy of Sciences 116 (45), 22445-22451, 2019
Mandates: US National Science Foundation, US Department of Energy, US Department of …
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
E Kaiser, JN Kutz, SL Brunton
Proceedings of the Royal Society A 474 (2219), 20180335, 2018
Mandates: US Department of Defense, Gordon and Betty Moore Foundation
Chaos as an intermittently forced linear system
SL Brunton, BW Brunton, JL Proctor, E Kaiser, JN Kutz
Nature communications 8 (1), 19, 2017
Mandates: US Department of Defense
Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
BW Brunton, LA Johnson, JG Ojemann, JN Kutz
Journal of neuroscience methods 258, 1-15, 2016
Mandates: US National Science Foundation, US National Institutes of Health
Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns
K Manohar, BW Brunton, JN Kutz, SL Brunton
IEEE Control Systems Magazine 38 (3), 63-86, 2018
Mandates: US Department of Defense
Data-driven discovery of Koopman eigenfunctions for control
E Kaiser, JN Kutz, SL Brunton
Machine Learning: Science and Technology 2 (3), 035023, 2021
Mandates: US National Science Foundation, US Department of Defense
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