Cosmin Safta
Cited by
Cited by
Dimensionality reduction for complex models via Bayesian compressive sensing
K Sargsyan, C Safta, HN Najm, BJ Debusschere, D Ricciuto, P Thornton
International Journal for Uncertainty Quantification 4 (1), 2014
Uncertainty quantification of reaction mechanisms accounting for correlations introduced by rate rules and fitted Arrhenius parameters
J Prager, HN Najm, K Sargsyan, C Safta, WJ Pitz
Combustion and flame 160 (9), 1583-1593, 2013
Uncertainty Quantification Toolkit.
B Debusschere
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2017
Efficient uncertainty quantification in stochastic economic dispatch
C Safta, RLY Chen, HN Najm, A Pinar, JP Watson
IEEE Transactions on Power Systems 32 (4), 2535-2546, 2016
TChem-a software toolkit for the analysis of complex kinetic models
C Safta, HN Najm, O Knio
Sandia Report, SAND2011-3282, 2011
Compressive sensing adaptation for polynomial chaos expansions
P Tsilifis, X Huan, C Safta, K Sargsyan, G Lacaze, JC Oefelein, HN Najm, ...
Journal of Computational Physics 380, 29-47, 2019
Autoignition and structure of nonpremixed CH4/H2 flames: detailed and reduced kinetic models
C Safta, CK Madnia
Combustion and flame 144 (1-2), 64-73, 2006
Uncertainty quantification given discontinuous model response and a limited number of model runs
K Sargsyan, C Safta, B Debusschere, H Najm
SIAM Journal on Scientific Computing 34 (1), B44-B64, 2012
A high-order low-Mach number AMR construction for chemically reacting flows
C Safta, J Ray, HN Najm
Journal of Computational Physics 229 (24), 9299-9322, 2010
Chemical model reduction under uncertainty
RM Galassi, M Valorani, HN Najm, C Safta, M Khalil, PP Ciottoli
Combustion and Flame 179, 242-252, 2017
Compressive sensing with cross-validation and stop-sampling for sparse polynomial chaos expansions
X Huan, C Safta, K Sargsyan, ZP Vane, G Lacaze, JC Oefelein, HN Najm
SIAM/ASA Journal on Uncertainty Quantification 6 (2), 907-936, 2018
Global sensitivity analysis and estimation of model error, toward uncertainty quantification in scramjet computations
X Huan, C Safta, K Sargsyan, G Geraci, MS Eldred, ZP Vane, G Lacaze, ...
AIAA Journal 56 (3), 1170-1184, 2018
A second-order coupled immersed boundary-SAMR construction for chemically reacting flow over a heat-conducting Cartesian grid-conforming solid
KS Kedia, C Safta, J Ray, HN Najm, AF Ghoniem
Journal of Computational Physics 272, 408-428, 2014
The first high-order CFD simulation of aircraft: challenges and opportunities
F Ladeinde, K Alabi, C Safta, X Cai, F Johnson
44th AIAA Aerospace Sciences Meeting and Exhibit, 1526, 2006
Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods
D Lu, D Ricciuto, A Walker, C Safta, W Munger
Biogeosciences 14 (18), 4295-4314, 2017
Interaction of a vortex ring with a diffusion flame
C Safta, S Enachescu, CK Madnia
Physics of Fluids 14 (2), 668-681, 2002
Data-free inference of uncertain parameters in chemical models
HN Najm, RD Berry, C Safta, K Sargsyan, BJ Debusschere
International Journal for Uncertainty Quantification 4 (2), 2014
Hybrid discrete/continuum algorithms for stochastic reaction networks
C Safta, K Sargsyan, B Debusschere, HN Najm
Journal of Computational Physics 281, 177-198, 2015
Enhancing model predictability for a scramjet using probabilistic learning on manifolds
C Soize, R Ghanem, C Safta, X Huan, ZP Vane, JC Oefelein, G Lacaze, ...
AIAA Journal 57 (1), 365-378, 2019
ULFM-MPI implementation of a resilient task-based partial differential equations preconditioner
F Rizzi, K Morris, K Sargsyan, P Mycek, C Safta, B Debusschere, ...
Proceedings of the ACM Workshop on Fault-Tolerance for HPC at Extreme Scale …, 2016
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