Raul Tempone

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Fabio NobileMATH - CSQI, Ecole Polytechnique Fédérale de Lausanne (EPFL)Verified email at epfl.ch
babuska iProfessor Emeritus, Oden Institute, University of Texas, AustinVerified email at ices.utexas.edu
Georgios ZourarisProfessor, University of CreteVerified email at uoc.gr
Erik von SchwerinUniversity of DelawareVerified email at udel.edu
Anders SzepessyKTH, StockholmVerified email at kth.se
Abdul-Lateef Haji-AliHeriot-Watt UniversityVerified email at hw.ac.uk
Prof. Mohamed-Slim AlouiniWireless CT Lab, King Abdullah University of Science and Technology (KAUST), Kingdom of Saudi ArabiaVerified email at kaust.edu.sa
Håkon HoelRWTH AachenVerified email at uq.rwth-aachen.de
Lorenzo TamelliniCNR-IMATIVerified email at imati.cnr.it
Nadhir Ben RachedPostdoctoral Research Scientist, RWTH Aachen UniversityVerified email at uq.rwth-aachen.de
Joakim BeckResearch Scientist, Uncertainty Quantification in Computational Science & Engineering, KAUSTVerified email at kaust.edu.sa
Clayton WebsterUniversity of Texas at AustinVerified email at utexas.edu
Abla KammounKAUSTVerified email at kaust.edu.sa
Marco ScavinoProfesor Agregado, Universidad de la República, UruguayVerified email at iesta.edu.uy
Christian BayerResearch fellow, Weierstrass Institute, BerlinVerified email at wias-berlin.de
Kody J. H. LawDepartment of Mathematics, University of ManchesterVerified email at manchester.ac.uk
Quan LongHoneywellVerified email at honeywell.com
Mohammad MotamedThe University of New MexicoVerified email at math.unm.edu
tembineLearning and Game Theory LabVerified email at landglab.com
Pedro VilanovaStevens Institute of TechnologyVerified email at stevens.edu

Raul Tempone

Professor of Applied Mathematics, Director - Center for Uncertainty Quantification, KAUST

Verified email at kaust.edu.sa - Homepage

Numerical Analysis Multilevel Monte Carlo Stochastic Differential Equations Uncertainty Quantification Stochastic Numerics


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A stochastic collocation method for elliptic partial differential equations with random input data I Babuška, F Nobile, R Tempone SIAM Journal on Numerical Analysis 45 (3), 1005-1034, 2007	1597	2007
A sparse grid stochastic collocation method for partial differential equations with random input data F Nobile, R Tempone, CG Webster SIAM Journal on Numerical Analysis 46 (5), 2309-2345, 2008	1138	2008
Galerkin finite element approximations of stochastic elliptic partial differential equations I Babuska, R Tempone, GE Zouraris SIAM Journal on Numerical Analysis 42 (2), 800-825, 2004	1047	2004
An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data F Nobile, R Tempone, CG Webster SIAM Journal on Numerical Analysis 46 (5), 2411-2442, 2008	638	2008
Solving elliptic boundary value problems with uncertain coefficients by the finite element method: the stochastic formulation I Babuška, R Tempone, GE Zouraris Computer methods in applied mechanics and engineering 194 (12-16), 1251-1294, 2005	386	2005
Stochastic spectral Galerkin and collocation methods for PDEs with random coefficients: a numerical comparison J Bäck, F Nobile, L Tamellini, R Tempone Spectral and high order methods for partial differential equations, 43-62, 2011	218	2011
On the optimal polynomial approximation of stochastic PDEs by Galerkin and collocation methods J Beck, R Tempone, F Nobile, L Tamellini Mathematical Models and Methods in Applied Sciences 22 (09), 1250023, 2012	153	2012
Multi-index Monte Carlo: when sparsity meets sampling AL Haji-Ali, F Nobile, R Tempone Numerische Mathematik 132 (4), 767-806, 2016	137	2016
A continuation multilevel Monte Carlo algorithm N Collier, AL Haji-Ali, F Nobile, E Von Schwerin, R Tempone BIT Numerical Mathematics 55 (2), 399-432, 2015	116	2015
Analysis and implementation issues for the numerical approximation of parabolic equations with random coefficients F Nobile, R Tempone International journal for numerical methods in engineering 80 (6‐7), 979-1006, 2009	116	2009
Discrete least squares polynomial approximation with random evaluations− application to parametric and stochastic elliptic PDEs A Chkifa, A Cohen, G Migliorati, F Nobile, R Tempone ESAIM: Mathematical Modelling and Numerical Analysis 49 (3), 815-837, 2015	111	2015
Fast estimation of expected information gains for Bayesian experimental designs based on Laplace approximations Q Long, M Scavino, R Tempone, S Wang Computer Methods in Applied Mechanics and Engineering 259, 24-39, 2013	105	2013
Analysis of Discrete L 2 Projection on Polynomial Spaces with Random Evaluations G Migliorati, F Nobile, E Von Schwerin, R Tempone Foundations of Computational Mathematics 14 (3), 419-456, 2014	100	2014
Theory and methodology for estimation and control of errors due to modeling, approximation, and uncertainty JT Oden, I Babuška, F Nobile, Y Feng, R Tempone Computer Methods in Applied Mechanics and Engineering 194 (2-5), 195-204, 2005	93	2005
A stochastic maximum principle for risk-sensitive mean-field type control B Djehiche, H Tembine, R Tempone IEEE Transactions on Automatic Control 60 (10), 2640-2649, 2015	89	2015
Adaptive weak approximation of stochastic differential equations A Szepessy, R Tempone, GE Zouraris Communications on Pure and Applied Mathematics: A Journal Issued by the …, 2001	89	2001
Multilevel sequential monte carlo samplers A Beskos, A Jasra, K Law, R Tempone, Y Zhou Stochastic Processes and their Applications 127 (5), 1417-1440, 2017	88	2017
Approximation of Quantities of Interest in Stochastic PDEs by the Random Discrete Projection on Polynomial Spaces G Migliorati, F Nobile, E von Schwerin, R Tempone SIAM Journal on Scientific Computing 35 (3), A1440-A1460, 2013	88	2013
A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria I Babuška, F Nobile, R Tempone Computer Methods in Applied Mechanics and Engineering 197 (29-32), 2517-2539, 2008	88	2008
Solving stochastic partial differential equations based on the experimental data I Babuška, KM Liu, R Tempone Mathematical Models and Methods in Applied Sciences 13 (03), 415-444, 2003	76	2003

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