| Hamiltonian neural networks for solving equations of motion M Mattheakis, D Sondak, AS Dogra, P Protopapas Physical Review E 105 (6), 065305, 2022 | 181 | 2022 |
| Neurodiffeq: A python package for solving differential equations with neural networks F Chen, D Sondak, P Protopapas, M Mattheakis, S Liu, D Agarwal, ... Journal of Open Source Software 5 (46), 1931, 2020 | 181 | 2020 |
| Physical symmetries embedded in neural networks M Mattheakis, P Protopapas, D Sondak, M Di Giovanni, E Kaxiras arXiv preprint arXiv:1904.08991, 2019 | 113 | 2019 |
| Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems S Desai, M Mattheakis, D Sondak, P Protopapas, S Roberts arXiv preprint arXiv:2107.08024, 2021 | 91 | 2021 |
| Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow R Fang, D Sondak, P Protopapas, S Succi Journal of Turbulence 21 (9-10), 525-543, 2020 | 76 | 2020 |
| Solving differential equations using neural network solution bundles C Flamant, P Protopapas, D Sondak arXiv preprint arXiv:2006.14372, 2020 | 52 | 2020 |
| Optimal heat transport solutions for Rayleigh–Bénard convection D Sondak, LM Smith, F Waleffe Journal of Fluid Mechanics 784, 565-595, 2015 | 50 | 2015 |
| Deep learning for turbulent channel flow R Fang, D Sondak, P Protopapas, S Succi arXiv preprint arXiv:1812.02241, 2018 | 28 | 2018 |
| A residual based eddy viscosity model for the large eddy simulation of turbulent flows AA Oberai, J Liu, D Sondak, TJR Hughes Computer Methods in Applied Mechanics and Engineering 282, 54-70, 2014 | 28 | 2014 |
| A new class of finite element variational multiscale turbulence models for incompressible magnetohydrodynamics D Sondak, JN Shadid, AA Oberai, RP Pawlowski, EC Cyr, TM Smith Journal of Computational Physics 295, 596-616, 2015 | 26 | 2015 |
| Can phoretic particles swim in two dimensions? D Sondak, C Hawley, S Heng, R Vinsonhaler, E Lauga, JL Thiffeault Physical Review E 94 (6), 062606, 2016 | 23 | 2016 |
| Convolutional neural network models and interpretability for the anisotropic reynolds stress tensor in turbulent one-dimensional flows H Sáez de Ocáriz Borde, D Sondak, P Protopapas Journal of Turbulence 23 (1-2), 1-28, 2022 | 22 | 2022 |
| Coherent solutions and transition to turbulence in two-dimensional Rayleigh-Bénard convection P Kooloth, D Sondak, LM Smith Physical Review Fluids 6 (1), 013501, 2021 | 18 | 2021 |
| Finding multiple solutions of odes with neural networks M Di Giovanni, D Sondak, P Protopapas, M Brambilla CEUR WORKSHOP PROCEEDINGS 2587, 1-7, 2020 | 14 | 2020 |
| Deqgan: Learning the loss function for pinns with generative adversarial networks B Bullwinkel, D Randle, P Protopapas, D Sondak arXiv preprint arXiv:2209.07081, 2022 | 13 | 2022 |
| Learning a reduced basis of dynamical systems using an autoencoder D Sondak, P Protopapas Physical Review E 104 (3), 034202, 2021 | 13 | 2021 |
| Large eddy simulation models for incompressible magnetohydrodynamics derived from the variational multiscale formulation D Sondak, AA Oberai Physics of Plasmas 19 (10), 2012 | 13* | 2012 |
| Application of the variational Germano identity to the variational multiscale formulation AA Oberai, D Sondak International journal for numerical methods in biomedical engineering 27 (2 …, 2011 | 12 | 2011 |
| Extending a physics-informed machine-learning network for superresolution studies of Rayleigh–Bénard convection DM Salim, B Burkhart, D Sondak The Astrophysical Journal 964 (1), 2, 2024 | 10 | 2024 |
| High Rayleigh number variational multiscale large eddy simulations of Rayleigh-Bénard convection D Sondak, TM Smith, RP Pawlowski, S Conde, JN Shadid Mechanics Research Communications 112, 103614, 2021 | 8 | 2021 |
Pavlos ProtopapasHarvardVerified email at seas.harvard.edu
