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Gavin D Portwood
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Turbulence forecasting via neural ode
GD Portwood, PP Mitra, MD Ribeiro, TM Nguyen, BT Nadiga, JA Saenz, ...
arXiv preprint arXiv:1911.05180, 2019
572019
Robust identification of dynamically distinct regions in stratified turbulence
GD Portwood, SM de Bruyn Kops, JR Taylor, H Salehipour, CP Caulfield
Journal of fluid mechanics 807, R2, 2016
512016
Asymptotic dynamics of high dynamic range stratified turbulence
GD Portwood, SM de Bruyn Kops, CP Caulfield
Physical review letters 122 (19), 194504, 2019
472019
Interpreting neural network models of residual scalar flux
GD Portwood, BT Nadiga, JA Saenz, D Livescu
Journal of Fluid Mechanics 907, A23, 2021
402021
Accelerating training in artificial neural networks with dynamic mode decomposition
ME Tano, GD Portwood, JC Ragusa
arXiv preprint arXiv:2006.14371, 2020
102020
Learning non-linear spatio-temporal dynamics with convolutional Neural ODEs
V Shankar, G Portwood, A Mohan, P Mitra, C Rackauckas, L Wilson, ...
Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), 2020
92020
Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow
V Shankar, GD Portwood, AT Mohan, PP Mitra, D Krishnamurthy, ...
Physics of Fluids 34 (11), 2022
52022
Analysis of scale-dependent kinetic and potential energy in sheared, stably stratified turbulence
X Zhang, R Dhariwal, G Portwood, SM de Bruyn Kops, AD Bragg
Journal of Fluid Mechanics 946, A6, 2022
52022
A data-driven approach to modeling turbulent decay at non-asymptotic Reynolds numbers
MD Ribeiro, GD Portwood, P Mitra, TM Nyugen, BT Nadiga, M Chertkov, ...
Bulletin of the American Physical Society, 2019
52019
Implications of inertial subrange scaling for stably stratified mixing
GD Portwood, SM de Bruyn Kops, CP Caulfield
Journal of Fluid Mechanics 939, A10, 2022
42022
Rapid spatiotemporal turbulence modeling with convolutional Neural ODEs
V Shankar, G Portwood, A Mohan, P Mitra, V Viswanathan, D Schmidt
APS Division of Fluid Dynamics Meeting Abstracts, X11. 004, 2020
32020
Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows
SF Lewin, SM Kops, GD Portwood, CP Caulfield
arXiv preprint arXiv:2112.01113, 2021
22021
Physics-informed deep neural networks applied to scalar subgrid flux modeling in a mixed DNS/LES framework
G Portwood, M Chertkov, B Nadiga, J Saenz, D Livescu
APS Division of Fluid Dynamics Meeting Abstracts, A19. 001, 2019
22019
A study on homogeneous sheared stably stratified turbulence
G Portwood
22019
Multigrid Solver With Super-Resolved Interpolation
F Holguin, GS Sidharth, G Portwood
arXiv preprint arXiv:2105.01739, 2021
12021
Autonomous RANS/LES hybrid models with data-driven subclosures
G Portwood, J Saenz, D Livescu
APS Division of Fluid Dynamics Meeting Abstracts, NP05. 164, 2019
12019
Unsupervised machine learning to teach fluid dynamicists to think in 15 dimensions
SM de Bruyn Kops, DJ Saunders, EA Rietman, GD Portwood
arXiv e-prints, arXiv: 1907.10035, 2019
12019
Toward direct numerical simulations of the stratified turbulence inertial range
S de Bruyn Kops, JJ Riley, GD Portwood
International Symposium on Stratified Flows 1 (1), 2016
12016
Accelerating multigrid solver with generative super-resolution
F Holguin, GS Sidharth, G Portwood
arXiv preprint arXiv:2403.07936, 2024
2024
A data-driven method for modelling dissipation rates in stratified turbulence
SF Lewin, SM de Bruyn Kops, PC Colm-cille, GD Portwood
Journal of Fluid Mechanics 977, A37, 2023
2023
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