Guang Lin
Guang Lin
Associate Dean for Research, Professor of Mathematics, Mech Eng, Statistics, Purdue University
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
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
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
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
Predicting shock dynamics in the presence of uncertainties
G Lin, CH Su, GE Karniadakis
Journal of Computational Physics 217 (1), 260-276, 2006
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
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
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
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
A Comparative Study of
YH Tang, G Lin, H Sun
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
A sensitivity study of radiative fluxes at the top of atmosphere to cloud-microphysics and aerosol parameters in the community atmosphere model CAM5
C Zhao, X Liu, Y Qian, J Yoon, Z Hou, G Lin, S McFarlane, H Wang, ...
Atmospheric Chemistry and Physics 13 (21), 10969-10987, 2013
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
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