| Modeling Spatial Extremes via Ensemble-of-Trees of Pairwise Copulas H Yu, WIT Uy, J Dauwels IEEE Transactions on Signal Processing 65 (3), 571 - 586, 2016 | 25 | 2016 |
| Operator inference of non-Markovian terms for learning reduced models from partially observed state trajectories WIT Uy, B Peherstorfer Journal of Scientific Computing 88 (3), 1-31, 2021 | 15 | 2021 |
| Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations WIT Uy, B Peherstorfer ESAIM: Mathematical Modelling and Numerical Analysis 55 (3), 735-761, 2021 | 15 | 2021 |
| Modeling extreme events in spatial domain by copula graphical models H Yu, Z Choo, WIT Uy, J Dauwels, P Jonathan 2012 15th International Conference on Information Fusion, 1761-1768, 2012 | 14 | 2012 |
| Operator inference with roll outs for learning reduced models from scarce and low-quality data WIT Uy, D Hartmann, B Peherstorfer Computers & Mathematics with Applications 145, 224-239, 2023 | 12 | 2023 |
| Copula Gaussian multiscale graphical models with application to geophysical modeling H Yu, J Dauwels, X Zhang, S Xu, WIT Uy 2012 15th International Conference on Information Fusion, 1741-1748, 2012 | 11 | 2012 |
| Active operator inference for learning low-dimensional dynamical-system models from noisy data WIT Uy, Y Wang, Y Wen, B Peherstorfer SIAM Journal on Scientific Computing 45 (4), A1462-A1490, 2023 | 10 | 2023 |
| Neural network representation of the probability density function of diffusion processes<? A3B2 show [editpick]?> WIT Uy, MD Grigoriu Chaos: An Interdisciplinary Journal of Nonlinear Science 30 (9), 093118, 2020 | 9 | 2020 |
| Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems WIT Uy, CR Wentland, C Huang, B Peherstorfer arXiv preprint arXiv:2209.06957, 2022 | 7 | 2022 |
| Monte Carlo computation of the Laplace transform of exponential Brownian functionals N Privault, WI Uy Methodology and Computing in Applied Probability 15 (3), 511-524, 2013 | 6 | 2013 |
| An adaptive method for solving stochastic equations based on interpolants over Voronoi cells WIT Uy, MD Grigoriu Probabilistic Engineering Mechanics 51, 23-41, 2018 | 4 | 2018 |
| Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems R Singh, WIT Uy, B Peherstorfer Chaos: An Interdisciplinary Journal of Nonlinear Science 33 (11), 2023 | 3 | 2023 |
| Discussion of “Extreme Events: Mechanisms and Prediction”(M. Farazmand and TP Sapsis, 2019, ASME Appl. Mech. Rev., 71 (5), p. 050801) MD Grigoriu, WIT Uy Applied Mechanics Reviews 71 (5), 2019 | 2 | 2019 |
| Specification of additional information for solving stochastic inverse problems WIT Uy, M Grigoriu SIAM Journal on Scientific Computing 41 (5), A2880-A2910, 2019 | 1 | 2019 |
| GenFormer: A Deep-Learning-Based Approach for Generating Multivariate Stochastic Processes H Zhao, WIT Uy arXiv preprint arXiv:2402.02010, 2024 | | 2024 |
| Identification of input random field samples causing extreme responses WIT Uy, MD Grigoriu Applied Mathematical Modelling 82, 916-937, 2020 | | 2020 |
| Discussion of “Extreme Events: Mechanisms and Prediction” by M. Farazmand and TP Sapsis (ASME, Applied Mechanics Reviews) M Grigoriu, WIT Uy | | |
Benjamin PeherstorferCourant Institute of Mathematical Sciences, New York UniversityVerified email at cims.nyu.edu
