| 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 | 532 | 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 | 249 | 2018 |
| Machine learning the kinematics of spherical particles in fluid flows ZY Wan, TP Sapsis Journal of Fluid Mechanics 857, R2, 2018 | 49 | 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 | 49 | 2017 |
| Debias coarsely, sample conditionally: Statistical downscaling through optimal transport and probabilistic diffusion models ZY Wan, R Baptista, A Boral, YF Chen, J Anderson, F Sha, ... Advances in Neural Information Processing Systems (NeurIPS) 36, 2024 | 14 | 2024 |
| Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems ZY Wan, L Zepeda-Núñez, A Boral, F Sha The Eleventh International Conference on Learning Representations (ICLR), 2023 | 13 | 2023 |
| A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets ZY Wan, B Dodov, C Lessig, H Dijkstra, TP Sapsis Journal of Computational Physics 442, 110484, 2021 | 13 | 2021 |
| 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 | 12 | 2020 |
| Neural ideal large eddy simulation: Modeling turbulence with neural stochastic differential equations A Boral, ZY Wan, L Zepeda-Núñez, J Lottes, Q Wang, Y Chen, J Anderson, ... Advances in Neural Information Processing Systems (NeurIPS) 36, 2024 | 6 | 2024 |
| 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 | 5 | 2013 |
| DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems Y Schiff, ZY Wan, JB Parker, S Hoyer, V Kuleshov, F Sha, ... The Forty-first International Conference on Machine Learning (ICML), 2024 | 4 | 2024 |
| Physics-constrained machine learning strategies for turbulent flows and bubble dynamics ZY Wan Massachusetts Institute of Technology, 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 | 2 | 2019 |
| A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data BB Sorensen, L Zepeda-Núñez, I Lopez-Gomez, ZY Wan, R Carver, ... arXiv preprint arXiv:2408.02688, 2024 | 1 | 2024 |
| Machine learning the kinematics of bubbles in fluid flows ZY Wan, TP Sapsis Submitted, 2018 | 1 | 2018 |
| Long-term durability of adhesively bonded composite joints under quasi-static and fatigue loading C Li, ZY Wan, G LaPlante, CD Rans, G Li 34th conference and the 28th symposium of the ICAF, Helsinki, Finland, 819-829, 2015 | 1 | 2015 |
| Dynamical-generative downscaling of climate model ensembles I Lopez-Gomez, ZY Wan, L Zepeda-Núñez, T Schneider, J Anderson, ... arXiv preprint arXiv:2410.01776, 2024 | | 2024 |
| Generative AI for fast and accurate Statistical Computation of Fluids R Molinaro, S Lanthaler, B Raonić, T Rohner, V Armegioiu, ZY Wan, ... arXiv preprint arXiv:2409.18359, 2024 | | 2024 |
| Reduced-space Gaussian process regression forecast for nonlinear dynamical systems ZY Wan Massachusetts Institute of Technology, 2016 | | 2016 |
| Fatigue disbond growth for an adhesively bonded composite joint under mixed mode I/II loading C Li, T Teng, ZY Wan, NG Young, G Li, CD Rans, G LaPlante SAMPE 2014, 1-15, 2014 | | 2014 |
Themistoklis SapsisProfessor of Mechanical and Ocean Engineering, MITVerified email at mit.edu
