| An efficient surrogate model for emulation and physics extraction of large eddy simulations S Mak, CL Sung, X Wang, ST Yeh, YH Chang, VR Joseph, V Yang, ... Journal of the American Statistical Association 113 (524), 1443-1456, 2018 | 114 | 2018 |
| Common proper orthogonal decomposition-based spatiotemporal emulator for design exploration ST Yeh, X Wang, CL Sung, S Mak, YH Chang, L Zhang, CFJ Wu, V Yang AIAA journal 56 (6), 2429-2442, 2018 | 30 | 2018 |
| A generalized Gaussian process model for computer experiments with binary time series CL Sung, Y Hung, W Rittase, C Zhu, CFJ Wu Journal of the American Statistical Association 115 (530), 945-956, 2020 | 21 | 2020 |
| Exploiting variance reduction potential in local Gaussian process search CL Sung, RB Gramacy, B Haaland Statistica Sinica 28 (2), 577-600, 2018 | 21* | 2018 |
| Kernel-smoothed proper orthogonal decomposition–based emulation for spatiotemporally evolving flow dynamics prediction YH Chang, L Zhang, X Wang, ST Yeh, S Mak, CL Sung, CF Jeff Wu, ... AIAA journal 57 (12), 5269-5280, 2019 | 20 | 2019 |
| Calibration for computer experiments with binary responses and application to cell adhesion study CL Sung, Y Hung, W Rittase, C Zhu, CFJ Wu Journal of the American Statistical Association 115 (532), 1664-1674, 2020 | 16 | 2020 |
| Multiresolution functional anova for large-scale, many-input computer experiments CL Sung, W Wang, M Plumlee, B Haaland Journal of the American Statistical Association 115 (530), 908-919, 2020 | 14 | 2020 |
| Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy CL Sung, Y Ji, S Mak, W Wang, T Tang SIAM/ASA Journal on Uncertainty Quantification 12 (1), 157-181, 2024 | 9* | 2024 |
| Data-Driven Modeling of General Fluid Density Under Subcritical and Supercritical Conditions M Zhou, W Chen, X Su, CL Sung, X Wang, Z Ren AIAA Journal 64 (4), 1519-1531, 2023 | 7 | 2023 |
| Calibration of inexact computer models with heteroscedastic errors CL Sung, BD Barber, BJ Walker SIAM/ASA Journal on Uncertainty Quantification 10 (4), 1733-1752, 2022 | 5* | 2022 |
| Functional-input gaussian processes with applications to inverse scattering problems CL Sung, W Wang, F Cakoni, I Harris, Y Hung arXiv preprint arXiv:2201.01682, 2022 | 4 | 2022 |
| Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19 CL Sung, Y Hung Journal of the Royal Statistical Society Series C: Applied Statistics 73 (1 …, 2024 | 3 | 2024 |
| A clustered Gaussian process model for computer experiments CL Sung, B Haaland, Y Hwang, S Lu Statistica Sinica 33 (2), 893-918, 2023 | 3* | 2023 |
| Estimating functional parameters for understanding the impact of weather and government interventions on COVID-19 outbreak CL Sung Annals of Applied Statistics 16 (4), 2505-2522, 2022 | 3 | 2022 |
| Spatial-temporal flow dynamics prediction with large design space via data-driven analysis and LES-based surrogate model YH Chang, L Zhang, X Wang, ST Yeh, S Mak, CL Sung, CFJ Wu, V Yang ILASS-Americas 29th Annual Conference on liquid atomization and spray …, 2017 | 3 | 2017 |
| Uncertainty quantification of flame transfer function under a bayesian framework Y Li, X Wang, S Mak, CL Sung, J Wu, V Yang 2018 AIAA Aerospace Sciences Meeting, 1187, 2018 | 2 | 2018 |
| Modeling of thermophysical properties and vapor-liquid equilibrium using Gaussian process regression M Zhou, C Ni, CL Sung, S Ding, X Wang International Journal of Heat and Mass Transfer 219, 124888, 2024 | 1 | 2024 |
| A review on computer model calibration CL Sung, R Tuo Wiley Interdisciplinary Reviews: Computational Statistics 16 (1), e1645, 2024 | 1 | 2024 |
| Mesh-clustered Gaussian process emulator for partial differential equation boundary value problems CL Sung, W Wang, L Ding, X Wang Technometrics, 2024 | 1* | 2024 |
| Modeling With Uncertainty Quantification Identifies Essential Features of a Non-Canonical Algal Carbon-Concentrating Mechanism AK Steensma, JAM Kaste, J Heo, D Orr, CL Sung, Y Shachar-Hill, ... bioRxiv, 2024.04. 12.589284, 2024 | | 2024 |
Simon MakAssistant Professor of Statistical Science, Duke University在 duke.edu 的电子邮件经过验证
