| Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks PR Vlachas, W Byeon, ZY Wan, TP Sapsis, P Koumoutsakos Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018 | 155 | 2018 |
| Data-assisted reduced-order modeling of extreme events in complex dynamical systems ZY Wan, P Vlachas, P Koumoutsakos, T Sapsis PloS one 13 (5), e0197704, 2018 | 91 | 2018 |
| Machine learning the kinematics of spherical particles in fluid flows ZY Wan, TP Sapsis Journal of Fluid Mechanics 857, 2018 | 21 | 2018 |
| Reduced-space Gaussian Process Regression for data-driven probabilistic forecast of chaotic dynamical systems ZY Wan, TP Sapsis Physica D: Nonlinear Phenomena 345, 40-55, 2017 | 18 | 2017 |
| Fatigue delamination growth for an adhesively-bonded composite joint under mode I loading C Li, T Teng, Z Wan, G Li, C Rans 27th ICAF Symposium. Jerusalem, 2013 | 3 | 2013 |
| Bubbles in turbulent flows: Data-driven, kinematic models with history terms ZY Wan, P Karnakov, P Koumoutsakos, TP Sapsis International Journal of Multiphase Flow 129, 103286, 2020 | 2 | 2020 |
| Data-assisted reduced-order modeling of climate dynamics. T Sapsis, ZY Wan, B Dodov, H Dijkstra, C Lessig Geophysical Research Abstracts 21, 2019 | 1 | 2019 |
| Physics-constrained machine learning strategies for turbulent flows and bubble dynamics ZY Wan Massachusetts Institute of Technology, 2020 | | 2020 |
| Complementing Imperfect Models with Data for the Prediction of Extreme Events in Turbulent Systems ZY Wan, T Sapsis Bulletin of the American Physical Society 63, 2018 | | 2018 |
| Reduced-space Gaussian process regression forecast for nonlinear dynamical systems ZY Wan Massachusetts Institute of Technology, 2016 | | 2016 |
Themistoklis SapsisAssociate Professor, MITVerified email at mit.edu
