Youssef Marzouk

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Habib NajmSandia National LaboratoriesPatvirtintas el. paštas sandia.gov
Ricardo BaptistaCalifornia Institute of TechnologyPatvirtintas el. paštas caltech.edu
Tiangang CuiUniversity of SydneyPatvirtintas el. paštas sydney.edu.au
Ahmed F GhoniemProfessor of Mechanical Engineering, Massachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Alessio SpantiniMassachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Olivier ZahmINRIAPatvirtintas el. paštas inria.fr
Karen WillcoxOden Institute for Computational Engineering and Sciences, UT AustinPatvirtintas el. paštas oden.utexas.edu
Matthew ParnoSolea EnergyPatvirtintas el. paštas soleaenergy.com
Bart van Bloemen WaandersSandia National LaboratoriesPatvirtintas el. paštas sandia.gov
Daniele BigoniData Science Team Leader at Ammagamma S.r.l.Patvirtintas el. paštas mit.edu
Jaideep RaySandia National LaboratoriesPatvirtintas el. paštas sandia.gov
Omar GhattasProfessor & Cockrell endowed chair, Mechanical Engineering & Oden Institute, Univ of Texas at AustinPatvirtintas el. paštas ices.utexas.edu
Xun HuanAssociate Professor of Mechanical Engineering, University of MichiganPatvirtintas el. paštas umich.edu
Tarek MoselhyMassachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Alex GorodetskyAssociate Professor of Aerospace Engineering, University of MichiganPatvirtintas el. paštas umich.edu
Kody J. H. LawProfessor at the University of Manchester and AI Research Scientist at MetaPatvirtintas el. paštas meta.com
Patrick R. ConradMassachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Sertac KaramanProfessor of Aeronautics and Astronautics, Massachusetts Institute of TechnologyPatvirtintas el. paštas mit.edu
Anne WhiteMITPatvirtintas el. paštas mit.edu
Martin GreenwaldMIT - Plasma Science & Fusion CenterPatvirtintas el. paštas alum.mit.edu

Stebėti

Youssef Marzouk

Professor, Massachusetts Institute of Technology

Patvirtintas el. paštas mit.edu - Pagrindinis puslapis

computational mathematics uncertainty quantification inverse problems data assimilation Bayesian statistics


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Stochastic spectral methods for efficient Bayesian solution of inverse problems YM Marzouk, HN Najm, LA Rahn Journal of Computational Physics 224 (2), 560-586, 2007	598	2007
Simulation-based optimal Bayesian experimental design for nonlinear systems X Huan, YM Marzouk Journal of Computational Physics 232 (1), 288-317, 2013	508	2013
Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems YM Marzouk, HN Najm Journal of Computational Physics 228 (6), 1862-1902, 2009	491	2009
Bayesian inference with optimal maps TA El Moselhy, YM Marzouk Journal of Computational Physics 231 (23), 7815-7850, 2012	355	2012
A stochastic collocation approach to Bayesian inference in inverse problems Y Marzouk, D Xiu Communications in Computational Physics 6 (4), 826-847, 2009	355	2009
Sampling via measure transport: An introduction Y Marzouk, T Moselhy, M Parno, A Spantini Handbook of uncertainty quantification 1, 2, 2016	289*	2016
Dimension-independent likelihood-informed MCMC T Cui, KJH Law, YM Marzouk Journal of Computational Physics 304, 109-137, 2016	226	2016
Large-scale inverse problems and quantification of uncertainty L Biegler, G Biros, O Ghattas, M Heinkenschloss, D Keyes, B Mallick, ... Wiley, 2011	217	2011
Data‐driven model reduction for the Bayesian solution of inverse problems T Cui, YM Marzouk, KE Willcox International Journal for Numerical Methods in Engineering 102 (5), 966-990, 2015	216	2015
Transport map accelerated Markov chain Monte Carlo M Parno, Y Marzouk SIAM/ASA Journal on Uncertainty Quantification 6 (2), 645–682, 2018	210	2018
Adaptive Smolyak pseudospectral approximations PR Conrad, YM Marzouk SIAM Journal on Scientific Computing 35 (6), A2643-A2670, 2013	205	2013
Likelihood-informed dimension reduction for nonlinear inverse problems T Cui, J Martin, YM Marzouk, A Solonen, A Spantini Inverse Problems 30 (11), 114015, 2014	194	2014
Surrogate and reduced‐order modeling: a comparison of approaches for large‐scale statistical inverse problems M Frangos, Y Marzouk, K Willcox, B van Bloemen Waanders Large‐Scale Inverse Problems and Quantification of Uncertainty, 123-149, 2010	190	2010
Uncertainty quantification in chemical systems HN Najm, BJ Debusschere, YM Marzouk, S Widmer, OP Le Maître International journal for numerical methods in engineering 80 (6‐7), 789-814, 2009	172	2009
Optimal low-rank approximations of Bayesian linear inverse problems A Spantini, A Solonen, T Cui, J Martin, L Tenorio, Y Marzouk SIAM Journal on Scientific Computing 37 (6), A2451-A2487, 2015	153	2015
Multifidelity importance sampling B Peherstorfer, T Cui, Y Marzouk, K Willcox Computer Methods in Applied Mechanics and Engineering 300, 490-509, 2016	150	2016
Inference via low-dimensional couplings A Spantini, D Bigoni, Y Marzouk Journal of Machine Learning Research 19 (66), 1-71, 2018	142	2018
Gradient-based stochastic optimization methods in Bayesian experimental design X Huan, YM Marzouk International Journal for Uncertainty Quantification 4 (6), 2014	141	2014
Accelerating asymptotically exact MCMC for computationally intensive models via local approximations PR Conrad, YM Marzouk, NS Pillai, A Smith Journal of the American Statistical Association 111 (516), 1591-1607, 2016	140	2016
A Stein variational Newton method G Detommaso, T Cui, Y Marzouk, A Spantini, R Scheichl Advances in Neural Information Processing Systems 31, 2018	139	2018

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