| Implementation of a fully nonlinear Hamiltonian Coupled-Mode Theory, and application to solitary wave problems over bathymetry CE Papoutsellis, AG Charalampopoulos, GA Athanassoulis European Journal of Mechanics-B/Fluids 72, 199-224, 2018 | 31 | 2018 |
| Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers ATG Charalampopoulos, TP Sapsis Physical Review Fluids 7 (2), 024305, 2022 | 20 | 2022 |
| A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populations SH Bryngelson, A Charalampopoulos, TP Sapsis, T Colonius International Journal of Multiphase Flow 127, 103262, 2020 | 15 | 2020 |
| Hybrid quadrature moment method for accurate and stable representation of non-Gaussian processes applied to bubble dynamics A Charalampopoulos, SH Bryngelson, T Colonius, TP Sapsis Philosophical Transactions of the Royal Society A 380 (2229), 20210209, 2022 | 8 | 2022 |
| Uncertainty quantification of turbulent systems via physically consistent and data-informed reduced-order models A Charalampopoulos, T Sapsis Physics of Fluids 34 (7), 2022 | 7 | 2022 |
| Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets A Charalampopoulos, S Zhang, B Harrop, LR Leung, TP Sapsis | 2 | 2023 |
| A Hamiltonian coupled mode method for the fully nonlinear water wave problem, including the case of a moving seabed AT Charalampopoulos | 1 | 2016 |
| Interaction of solitary water waves with uneven bottom using a Hamiltonian-Coupled Mode System C Papoutsellis, G Athanassoulis, A Charalampopoulos Frontiers in Nonlinear Physics, 2016 | 1 | 2016 |
| A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics BB Sorensen, A Charalampopoulos, S Zhang, B Harrop, R Leung, ... arXiv preprint arXiv:2402.18484, 2024 | | 2024 |
| A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics B Barthel Sorensen, A Charalampopoulos, S Zhang, B Harrop, R Leung, ... arXiv e-prints, arXiv: 2402.18484, 2024 | | 2024 |
| Quantifying the Value of Data in Scientific Machine Learning Models with Output-Weighted Active Learning B Champenois, A Charalampopoulos, T Sapsis AGU23, 2023 | | 2023 |
| A Machine Learning Bias Correction of Large-scale Environment of Extreme Weather Events in E3SM Atmosphere Model S Zhang, BE Harrop, LR Leung, AT Charalampopoulos, B Barthel, WW Xu, ... Authorea Preprints, 2023 | | 2023 |
| Statistics of extreme events in climate models via coarse-scale simulations and machine learning correction operators based on nudged datasets AT Charalampopoulos, S Zhang, B Harrop, R Leung, T Sapsis | | 2023 |
| Coarse-grained models for prediction, uncertainty quantification, and extreme event statistics of turbulent flows in engineering and geophysical settings using physics … AT Charalampopoulos Massachusetts Institute of Technology, 2023 | | 2023 |
| Data-assisted uncertainty quantification and extreme event prediction in climate models using physically-consistent neural networks. AT Charalampopoulos, S Zhang, R Leung, T Sapsis Bulletin of the American Physical Society 67, 2022 | | 2022 |
| Uncertainty quantification and extreme event analysis for turbulent flows using energy-preserving data-driven closure schemes AT Charalampopoulos, T Sapsis APS Division of Fluid Dynamics Meeting Abstracts, T02. 003, 2021 | | 2021 |
| Bypassing quadrature moment method instability via recurrent neural networks with application to cavitating bubble dispersions S Bryngelson, AT Charalampopoulos, R Fox, T Sapsis, T Colonius APS Division of Fluid Dynamics Meeting Abstracts, Q26. 003, 2021 | | 2021 |
| Machine-learning quasilinear Gaussian moment closures for uncertainty quantification of turbulent fluid flows AT Charalampopoulos, T Sapsis APS Division of Fluid Dynamics Meeting Abstracts, R10. 008, 2020 | | 2020 |
| Neural-network-augmented Gaussian moment method for the statistics of cavitating bubble populations S Bryngelson, A Charalampopoulos, T Sapsis, T Colonius APS Division of Fluid Dynamics Meeting Abstracts, S02. 007, 2019 | | 2019 |
| Machine learning non-local closures for turbulent anisotropic multiphase flows AT Charalampopoulos Massachusetts Institute of Technology, 2019 | | 2019 |
Themistoklis SapsisProfessor of Mechanical and Ocean Engineering, MITVerified email at mit.edu
