| Robust data-driven discovery of governing physical laws with error bars S Zhang, G Lin Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018 | 208 | 2018 |
| Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification I Bilionis, N Zabaras, BA Konomi, G Lin Journal of Computational Physics 241, 212-239, 2013 | 208 | 2013 |
| Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements I Bright, G Lin, JN Kutz Physics of Fluids 25 (12), 2013 | 187 | 2013 |
| Adaptive ANOVA decomposition of stochastic incompressible and compressible flows X Yang, M Choi, G Lin, GE Karniadakis Journal of Computational Physics 231 (4), 1587-1614, 2012 | 159 | 2012 |
| Some issues in uncertainty quantification and parameter tuning: A case study of convective parameterization scheme in the WRF regional climate model B Yang, Y Qian, G Lin, R Leung, Y Zhang Atmospheric Chemistry and Physics 12 (5), 2409-2427, 2012 | 155 | 2012 |
| An efficient, high-order probabilistic collocation method on sparse grids for three-dimensional flow and solute transport in randomly heterogeneous porous media G Lin, AM Tartakovsky Advances in Water Resources 32 (5), 712-722, 2009 | 143 | 2009 |
| Uncertainty quantification and parameter tuning in the CAM5 Zhang‐McFarlane convection scheme and impact of improved convection on the global circulation and climate B Yang, Y Qian, G Lin, LR Leung, PJ Rasch, GJ Zhang, SA McFarlane, ... Journal of Geophysical Research: Atmospheres 118 (2), 395-415, 2013 | 131 | 2013 |
| Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model Z Hou, M Huang, LR Leung, G Lin, DM Ricciuto Journal of Geophysical Research: Atmospheres 117 (D15), 2012 | 129 | 2012 |
| Multi-resolution climate ensemble parameter analysis with nested parallel coordinates plots J Wang, X Liu, HW Shen, G Lin IEEE transactions on visualization and computer graphics 23 (1), 81-90, 2016 | 125 | 2016 |
| ConvPDE-UQ: Convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains N Winovich, K Ramani, G Lin Journal of Computational Physics 394, 263-279, 2019 | 119 | 2019 |
| Generating random earthquake events for probabilistic tsunami hazard assessment RJ LeVeque, K Waagan, FI González, D Rim, G Lin Global Tsunami Science: Past and Future, Volume I, 3671-3692, 2017 | 108 | 2017 |
| Deeplight: Deep lightweight feature interactions for accelerating ctr predictions in ad serving W Deng, J Pan, T Zhou, D Kong, A Flores, G Lin Proceedings of the 14th ACM international conference on Web search and data …, 2021 | 104 | 2021 |
| Identifiability and predictability of integer-and fractional-order epidemiological models using physics-informed neural networks E Kharazmi, M Cai, X Zheng, Z Zhang, G Lin, GE Karniadakis Nature Computational Science 1 (11), 744-753, 2021 | 96 | 2021 |
| An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions J Zhang, G Lin, W Li, L Wu, L Zeng Water Resources Research 54 (3), 1716-1733, 2018 | 87 | 2018 |
| Predicting shock dynamics in the presence of uncertainties G Lin, CH Su, GE Karniadakis Journal of Computational Physics 217 (1), 260-276, 2006 | 83 | 2006 |
| Weak Galerkin finite element methods for Darcy flow: Anisotropy and heterogeneity G Lin, J Liu, L Mu, X Ye Journal of computational physics 276, 422-437, 2014 | 79 | 2014 |
| Dynamic-feature extraction, attribution, and reconstruction (DEAR) method for power system model reduction S Wang, S Lu, N Zhou, G Lin, M Elizondo, MA Pai IEEE transactions on power systems 29 (5), 2049-2059, 2014 | 77 | 2014 |
| Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids G Lin, AM Tartakovsky, DM Tartakovsky Journal of Computational Physics 229 (19), 6995-7012, 2010 | 75 | 2010 |
| A Comparative Study of YH Tang, G Lin, H Sun | 73 | 2021 |
| Improving simulation efficiency of MCMC for inverse modeling of hydrologic systems with a Kalman‐inspired proposal distribution J Zhang, JA Vrugt, X Shi, G Lin, L Wu, L Zeng Water Resources Research 56 (3), e2019WR025474, 2020 | 69 | 2020 |
George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering確認したメール アドレス: brown.edu
