John Jakeman

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Alex GorodetskyAssociate Professor of Aerospace Engineering, University of MichiganEmail yang diverifikasi di umich.edu
Gianluca GeraciSandia National LaboratoriesEmail yang diverifikasi di sandia.gov
Tim WildeySandia National LabsEmail yang diverifikasi di sandia.gov
Akil NarayanUniversity of UtahEmail yang diverifikasi di sci.utah.edu
Tom SeidlSenior Member of the Technical Staff, Sandia National LaboratoriesEmail yang diverifikasi di sandia.gov
Dongbin XiuProfessor of Mathematics, The Ohio State UniversityEmail yang diverifikasi di osu.edu
Laura P. SwilerSandia National LaboratoriesEmail yang diverifikasi di sandia.gov
A J JakemanProfessor, Australian National UniversityEmail yang diverifikasi di anu.edu.au
Barry CrokeAustralian National UniversityEmail yang diverifikasi di anu.edu.au
Cosmin SaftaSandia National LabsEmail yang diverifikasi di sandia.gov
Stephen G RobertsMathematics, Australian National UniversityEmail yang diverifikasi di anu.edu.au
Joseph GuillaumeAustralian National UniversityEmail yang diverifikasi di anu.edu.au
Troy ButlerUniversity of Colorado DenverEmail yang diverifikasi di ucdenver.edu
Mamikon GulianBooz Allen HamiltonEmail yang diverifikasi di bah.com
Tong QinApplied Scientist-Lab126@AmazonEmail yang diverifikasi di amazon.com
Philip BrunnerUniversity of NeuchâtelEmail yang diverifikasi di unine.ch
Stefano Marsili-LibelliUniversity of FlorenceEmail yang diverifikasi di unifi.it
Karel J. KeesmanProfessor (personal chair), Wageningen UniversityEmail yang diverifikasi di wur.nl
Tao ZhouProfessor of Mathematics, Chinese Academy of SciencesEmail yang diverifikasi di lsec.cc.ac.cn
Rick ArchibaldORNLEmail yang diverifikasi di ornl.gov

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John Jakeman

Principal Member of Technical Staff, Sandia National Laboratories

Email yang diverifikasi di sandia.gov - Beranda

Machine learning Uncertainty Quantification Multi-Fidelity Optimal Experimental Design


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: version 6.13 user's … BM Adams, WJ Bohnhoff, KR Dalbey, MS Ebeida, JP Eddy, MS Eldred, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020	1789*	2020
The future of sensitivity analysis: an essential discipline for systems modeling and policy support S Razavi, A Jakeman, A Saltelli, C Prieur, B Iooss, E Borgonovo, ... Environmental Modelling & Software 137, 104954, 2021	468	2021
Enhancing ℓ1-minimization estimates of polynomial chaos expansions using basis selection JD Jakeman, MS Eldred, K Sargsyan Journal of Computational Physics 289, 18-34, 2015	207	2015
Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose JHA Guillaume, JD Jakeman, S Marsili-Libelli, M Asher, P Brunner, ... Environmental Modelling & Software 119, 418-432, 2019	179	2019
A survey of constrained Gaussian process regression: Approaches and implementation challenges LP Swiler, M Gulian, AL Frankel, C Safta, JD Jakeman Journal of Machine Learning for Modeling and Computing 1 (2), 2020	173	2020
A generalized approximate control variate framework for multifidelity uncertainty quantification AA Gorodetsky, G Geraci, MS Eldred, JD Jakeman Journal of Computational Physics 408, 109257, 2020	152	2020
Adaptive Leja sparse grid constructions for stochastic collocation and high-dimensional approximation A Narayan, JD Jakeman SIAM Journal on Scientific Computing 36 (6), A2952-A2983, 2014	143	2014
Numerical approach for quantification of epistemic uncertainty J Jakeman, M Eldred, D Xiu Journal of Computational Physics 229 (12), 4648-4663, 2010	136	2010
Polynomial chaos expansions for dependent random variables JD Jakeman, F Franzelin, A Narayan, M Eldred, D Plfüger Computer Methods in Applied Mechanics and Engineering 351, 643-666, 2019	98	2019
A Christoffel function weighted least squares algorithm for collocation approximations A Narayan, J Jakeman, T Zhou Mathematics of Computation 86 (306), 1913-1947, 2017	98	2017
A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions JD Jakeman, A Narayan, T Zhou SIAM Journal on Scientific Computing 39 (3), A1114-A1144, 2017	83	2017
Local and dimension adaptive stochastic collocation for uncertainty quantification JD Jakeman, SG Roberts Sparse grids and applications, 181-203, 2012	83*	2012
Characterization of discontinuities in high-dimensional stochastic problems on adaptive sparse grids JD Jakeman, R Archibald, D Xiu Journal of Computational Physics 230 (10), 3977-3997, 2011	83	2011
Combining push-forward measures and Bayes' rule to construct consistent solutions to stochastic inverse problems T Butler, J Jakeman, T Wildey SIAM Journal on Scientific Computing 40 (2), A984-A1011, 2018	79*	2018
Modeling water quality in watersheds: From here to the next generation B Fu, JS Horsburgh, AJ Jakeman, C Gualtieri, T Arnold, L Marshall, ... Water Resources Research 56 (11), e2020WR027721, 2020	78	2020
Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions JD Jakeman, A Narayan, D Xiu Journal of Computational Physics 242, 790-808, 2013	71	2013
Non-destructive simulation of node defects in additively manufactured lattice structures B Lozanovski, D Downing, R Tino, A du Plessis, P Tran, J Jakeman, ... Additive Manufacturing 36, 101593, 2020	67	2020
Data-driven learning of nonautonomous systems T Qin, Z Chen, JD Jakeman, D Xiu SIAM Journal on Scientific Computing 43 (3), A1607-A1624, 2021	60	2021
Adaptive multi‐index collocation for uncertainty quantification and sensitivity analysis JD Jakeman, MS Eldred, G Geraci, A Gorodetsky International Journal for Numerical Methods in Engineering 121 (6), 1314-1343, 2020	59	2020
Deep learning of parameterized equations with applications to uncertainty quantification T Qin, Z Chen, JD Jakeman, D Xiu International Journal for Uncertainty Quantification 11 (2), 2021	52*	2021

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