| Turbulence forecasting via neural ode GD Portwood, PP Mitra, MD Ribeiro, TM Nguyen, BT Nadiga, JA Saenz, ... arXiv preprint arXiv:1911.05180, 2019 | 72 | 2019 |
| 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 | 58 | 2016 |
| Asymptotic dynamics of high dynamic range stratified turbulence GD Portwood, SM de Bruyn Kops, CP Caulfield Physical review letters 122 (19), 194504, 2019 | 51 | 2019 |
| Interpreting neural network models of residual scalar flux GD Portwood, BT Nadiga, JA Saenz, D Livescu Journal of Fluid Mechanics 907, A23, 2021 | 42 | 2021 |
| Accelerating training in artificial neural networks with dynamic mode decomposition ME Tano, GD Portwood, JC Ragusa arXiv preprint arXiv:2006.14371, 2020 | 13 | 2020 |
| 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 | 12 | 2020 |
| 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 | 8 | 2022 |
| 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 | 5 | 2022 |
| Implications of inertial subrange scaling for stably stratified mixing GD Portwood, SM de Bruyn Kops, CP Caulfield Journal of Fluid Mechanics 939, A10, 2022 | 5 | 2022 |
| 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 | 5 | 2019 |
| 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 | 3 | 2020 |
| 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 | 2 | 2023 |
| 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 | 2 | 2021 |
| 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 | 2 | 2019 |
| A study on homogeneous sheared stably stratified turbulence G Portwood | 2 | 2019 |
| Multigrid Solver With Super-Resolved Interpolation F Holguin, GS Sidharth, G Portwood arXiv preprint arXiv:2105.01739, 2021 | 1 | 2021 |
| 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 | 1 | 2019 |
| 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 | 1 | 2019 |
| 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 | 1 | 2016 |
| Accelerating multigrid solver with generative super-resolution F Holguin, GS Sidharth, G Portwood arXiv preprint arXiv:2403.07936, 2024 | | 2024 |
