Guang Lin

Hivatkozott rá

	Összes	2020 óta
Hivatkozások	6592	4345
h-index	44	34
i10-index	157	123

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Társszerzők

George Em KarniadakisThe Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and EngineeringE-mail megerősítve itt: brown.edu
Bledar Alexandros KonomiAssociate Professor, Department of Mathematical Sciences, University of CincinnatiE-mail megerősítve itt: uc.edu
Faming LiangDistinguished Professor of Statistics, Purdue UniversityE-mail megerősítve itt: purdue.edu
Zhangshuan (Jason) HouPacific Northwest National LaboratoryE-mail megerősítve itt: pnnl.gov
Ben YangSchool of Atmospheric Sciences, Nanjing UniversityE-mail megerősítve itt: nju.edu.cn
Weixuan LiMicrosoftE-mail megerősítve itt: microsoft.com
Alexandre TartakovskyUniversity of Illinois Urbana-Champaign and Pacific Northwest National LaboratoryE-mail megerősítve itt: pnnl.gov
Yun QianPNNLE-mail megerősítve itt: pnnl.gov
Bin ZhengScientist, Pacific Northwest National LaboratoryE-mail megerősítve itt: pnnl.gov
Shuai LuFounder and CEO, EnerModE-mail megerősítve itt: enermod.com
Ning ZhouAssociate Professor, SUNY Binghamton UniversityE-mail megerősítve itt: binghamton.edu
Jie BaoResearch Engineer, Experimental and Computational Engineering Group, Pacific Northwest NationalE-mail megerősítve itt: pnnl.gov
Chun ZhaoUniversity of Science and Technology of ChinaE-mail megerősítve itt: ustc.edu.cn
Shaobu WangPacific Northwest National LabE-mail megerősítve itt: pnnl.gov
Xiaoliang WanLouisiana State UniversityE-mail megerősítve itt: math.lsu.edu
J. Nathan KutzProfessor of Applied Mathematics & Electrical and Computer EngineeringE-mail megerősítve itt: uw.edu
Xiaohong LiuReta Haynes Chair in Geosciences, Professor of Atmospheric Sciences, Texas A&M UniversityE-mail megerősítve itt: tamu.edu
Hailong WangPacific Northwest National LaboratoryE-mail megerősítve itt: pnnl.gov
Minghuai WangSchool of Atmospheric Sciences, Nanjing UniversityE-mail megerősítve itt: nju.edu.cn
Marcelo A ElizondoPacific Northwest National LaboratoryE-mail megerősítve itt: pnnl.gov

Követés

Guang Lin

Associate Dean for Research, Professor of Mathematics, Mech Eng, Statistics, Purdue University

E-mail megerősítve itt: purdue.edu - Kezdőlap

Scientific Machine Learning Uncertainty Quantification Generative AI LLM Deep Learning


Cím Rendezés hivatkozások szerint Rendezés év szerint Rendezés cím szerint	Hivatkozott rá Hivatkozott rá	Év
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
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	69	2013

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