| Conceptual dynamical models for turbulence AJ Majda, Y Lee Proceedings of the National Academy of Sciences 111 (18), 6548-6553, 2014 | 44 | 2014 |
| State estimation and prediction using clustered particle filters Y Lee, AJ Majda Proceedings of the National Academy of Sciences 113 (51), 14609-14614, 2016 | 31 | 2016 |
| Numerical schemes for stochastic backscatter in the inverse cascade of quasigeostrophic turbulence I Grooms, Y Lee, AJ Majda Multiscale Modeling and Simulations 13 (3), 1001-1021, 2015 | 30 | 2015 |
| Ensemble Kalman filters for dynamical systems with unresolved turbulence I Grooms, Y Lee, AJ Majda Journal of Computational Physics 273, 435-452, 2014 | 24 | 2014 |
| A multiscale method for highly oscillatory dynamical systems using a Poincaré map type technique G Ariel, B Engquist, S Kim, Y Lee, R Tsai Journal of Scientific Computing 54, 247-268, 2013 | 24 | 2013 |
| Ensemble filtering and low-resolution model error: Covariance inflation, stochastic parameterization, and model numerics I Grooms, Y Lee, AJ Majda Monthly Weather Review 143 (10), 3912-3924, 2015 | 20 | 2015 |
| Multiscale methods for data assimilation in turbulence Y Lee, AJ Majda Multiscale Modeling and Simulations 13 (2), 691-713, 2015 | 17 | 2015 |
| Preventing catastrophic filter divergence using adaptive additive inflation for baroclinic turbulence Y Lee, AJ Majda, D Qi Monthly Weather Review 145 (2), 669-682, 2017 | 16 | 2017 |
| Variable step size multiscale methods for stiff and highly oscillatory dynamical systems Y Lee, B Engquist Discrete Contin. Dyn. Syst 34 (3), 1079–1097, 2014 | 12 | 2014 |
| Regularization for Ensemble Kalman Inversion Y Lee SIAM Journal on Scientific Computing 43 (5), A3417-A3437, 2021 | 11 | 2021 |
| Stochastic superparameterization and multiscale filtering of turbulent tracers Y Lee, AJ Majda, D Qi Multiscale Modeling & Simulation 15 (1), 215-234, 2017 | 11 | 2017 |
| Multiscale numerical methods for passive advection–diffusion in incompressible turbulent flow fields Y Lee, B Engquist Journal of Computational Physics 317, 33-46, 2016 | 11 | 2016 |
| Hierarchical learning to solve pdes using physics-informed neural networks J Han, Y Lee International Conference on Computational Science, 548-562, 2023 | 9* | 2023 |
| A framework for variational data assimilation with superparameterization I Grooms, Y Lee Nonlinear Processes in Geophysics 22 (5), 601-611, 2015 | 8 | 2015 |
| A neural network approach for homogenization of multiscale problems J Han, Y Lee Multiscale Modeling & Simulation 21 (2), 716-734, 2023 | 6 | 2023 |
| Sampling error correction in ensemble Kalman inversion Y Lee arXiv preprint arXiv:2105.11341, 2021 | 4 | 2021 |
| Fast integrators for dynamical systems with several temporal scales Y Lee, B Engquist arXiv preprint arXiv:1510.05728, 2015 | 4 | 2015 |
| Improving numerical accuracy for the viscous-plastic formulation of sea ice T Li, A Gelb, Y Lee Journal of Computational Physics 487, 112184, 2023 | 2 | 2023 |
| Inhomogeneous regularization with limited and indirect data J Han, Y Lee Journal of Computational and Applied Mathematics 428, 115193, 2023 | 1 | 2023 |
| Variational data assimilation with superparameterization I Grooms, Y Lee Nonlinear Processes in Geophysics Discussions 2 (2), 513-536, 2015 | 1 | 2015 |
Ian GroomsUniversity of Colorado, BoulderVerified email at colorado.edu
