Articles with public access mandates - Alireza DoostanLearn more
Not available anywhere: 5
A bi-fidelity approach for uncertainty quantification of heat transfer in a rectangular ribbed channel
A Doostan, G Geraci, G Iaccarino
Turbo Expo: Power for Land, Sea, and Air 49712, V02CT45A031, 2016
Mandates: US National Science Foundation, US Department of Energy
Influence of non-boltzmann radiation around titan atmospheric entry vehicles
SM Jo, P Rostkowski, A Doostan, JG Kim, M Panesi
AIAA AVIATION 2022 Forum, 3576, 2022
Mandates: US National Aeronautics and Space Administration
Generalized non-linear eddy viscosity models for data-assisted Reynolds stress closure
B Parmar, E Peters, KE Jansen, A Doostan, JA Evans
AIAA Scitech 2020 Forum, 0351, 2020
Mandates: US National Science Foundation
An evaluation of multi-fidelity modeling efficiency on a parametric study of naca airfoils
R Skinner, A Doostan, E Peters, J Evans, KE Jansen
35th AIAA Applied Aerodynamics Conference, 3260, 2017
Mandates: US National Science Foundation, US Department of Energy, US Department of …
Partitioned Solution of Coupled Stochastic Problems
M Hadigol, A Doostan, HG Matthies, R Niekamp
Numerical Simulations of Coupled Problems in Engineering, 405-422, 2014
Mandates: German Research Foundation
Available somewhere: 58
Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies
J Hampton, A Doostan
Journal of Computational Physics 280, 363-386, 2015
Mandates: US Department of Energy
Least squares polynomial chaos expansion: A review of sampling strategies
M Hadigol, A Doostan
Computer Methods in Applied Mechanics and Engineering 332, 382-407, 2018
Mandates: US National Science Foundation, US Department of Energy
Coherence motivated sampling and convergence analysis of least squares polynomial Chaos regression
J Hampton, A Doostan
Computer Methods in Applied Mechanics and Engineering 290, 73-97, 2015
Mandates: US Department of Energy
Sparse polynomial chaos expansions via compressed sensing and D-optimal design
P Diaz, A Doostan, J Hampton
Computer Methods in Applied Mechanics and Engineering 336, 640-666, 2018
Mandates: US National Science Foundation, US Department of Energy, US Department of …
A simple and efficient preconditioning scheme for heaviside enriched XFEM
C Lang, D Makhija, A Doostan, K Maute
Computational Mechanics 54, 1357-1374, 2014
Mandates: US Department of Energy
On polynomial chaos expansion via gradient-enhanced ℓ1-minimization
J Peng, J Hampton, A Doostan
Journal of Computational Physics 310, 440-458, 2016
Mandates: US National Science Foundation, US Department of Energy
Sparse identification of nonlinear dynamical systems via reweighted ℓ1-regularized least squares
A Cortiella, KC Park, A Doostan
Computer Methods in Applied Mechanics and Engineering 376, 113620, 2021
Mandates: US National Science Foundation
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation
S De, J Britton, M Reynolds, R Skinner, K Jansen, A Doostan
International Journal for Uncertainty Quantification 10 (6), 2020
Mandates: US National Science Foundation, US Department of Energy, US Department of …
Stochastic identification of composite material properties from limited experimental databases, Part II: Uncertainty modelling
L Mehrez, A Doostan, D Moens, D Vandepitte
Mechanical Systems and Signal Processing 27, 484-498, 2012
Mandates: Research Foundation (Flanders)
Postmaneuver collision probability estimation using sparse polynomial chaos expansions
BA Jones, N Parrish, A Doostan
Journal of Guidance, Control, and Dynamics 38 (8), 1425-1437, 2015
Mandates: US Department of Energy, US National Aeronautics and Space Administration
On uncertainty quantification of lithium-ion batteries: Application to an LiC6/LiCoO2 cell
M Hadigol, K Maute, A Doostan
Journal of Power Sources 300, 507-524, 2015
Mandates: US Department of Energy
Basis adaptive sample efficient polynomial chaos (BASE-PC)
J Hampton, A Doostan
Journal of Computational Physics 371, 20-49, 2018
Mandates: US National Science Foundation, US Department of Energy, US Department of …
A low-rank control variate for multilevel Monte Carlo simulation of high-dimensional uncertain systems
HR Fairbanks, A Doostan, C Ketelsen, G Iaccarino
Journal of Computational Physics 341, 121-139, 2017
Mandates: US National Science Foundation, US Department of Energy
Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction
J Hampton, HR Fairbanks, A Narayan, A Doostan
Journal of Computational Physics 368, 315-332, 2018
Mandates: US National Science Foundation, US Department of Energy, US Department of …
Time‐dependent global sensitivity analysis with active subspaces for a lithium ion battery model
PG Constantine, A Doostan
Statistical Analysis and Data Mining: The ASA Data Science Journal 10 (5 …, 2017
Mandates: US National Science Foundation, US Department of Energy, US Department of …
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