| A non-adapted sparse approximation of PDEs with stochastic inputs A Doostan, H Owhadi Journal of Computational Physics 230 (8), 3015-3034, 2011 | 505 | 2011 |
| Stochastic model reduction for chaos representations A Doostan, RG Ghanem, J Red-Horse Computer methods in applied mechanics and engineering 196 (37-40), 3951-3966, 2007 | 245 | 2007 |
| Nonlinear propagation of orbit uncertainty using non-intrusive polynomial chaos BA Jones, A Doostan, GH Born Journal of Guidance, Control, and Dynamics 36 (2), 430-444, 2013 | 238 | 2013 |
| Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies J Hampton, A Doostan Journal of Computational Physics 280, 363-386, 2015 | 237 | 2015 |
| A weighted ℓ1-minimization approach for sparse polynomial chaos expansions J Peng, J Hampton, A Doostan Journal of Computational Physics 267, 92-111, 2014 | 234 | 2014 |
| On the construction and analysis of stochastic models: characterization and propagation of the errors associated with limited data RG Ghanem, A Doostan Journal of Computational Physics 217 (1), 63-81, 2006 | 231 | 2006 |
| 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 | 182 | 2018 |
| A least-squares approximation of partial differential equations with high-dimensional random inputs A Doostan, G Iaccarino Journal of computational physics 228 (12), 4332-4345, 2009 | 180 | 2009 |
| 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 | 165 | 2015 |
| Non-intrusive low-rank separated approximation of high-dimensional stochastic models A Doostan, AA Validi, G Iaccarino Computer Methods in Applied Mechanics and Engineering 263, 42-55, 2013 | 118 | 2013 |
| 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 | 116 | 2018 |
| A simple and efficient preconditioning scheme for heaviside enriched XFEM C Lang, D Makhija, A Doostan, K Maute Computational Mechanics 54, 1357-1374, 2014 | 112 | 2014 |
| Optimum seismic design of concentrically braced steel frames: concepts and design procedures H Moghaddam, I Hajirasouliha, A Doostan Journal of Constructional Steel Research 61 (2), 151-166, 2005 | 107 | 2005 |
| Padé–Legendre approximants for uncertainty analysis with discontinuous response surfaces T Chantrasmi, A Doostan, G Iaccarino Journal of Computational Physics 228 (19), 7159-7180, 2009 | 106 | 2009 |
| On polynomial chaos expansion via gradient-enhanced ℓ1-minimization J Peng, J Hampton, A Doostan Journal of Computational Physics 310, 440-458, 2016 | 102 | 2016 |
| A probabilistic construction of model validation RG Ghanem, A Doostan, J Red-Horse Computer Methods in Applied Mechanics and Engineering 197 (29-32), 2585-2595, 2008 | 98 | 2008 |
| Satellite collision probability estimation using polynomial chaos expansions BA Jones, A Doostan Advances in Space Research 52 (11), 1860-1875, 2013 | 97 | 2013 |
| 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 | 81 | 2021 |
| 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 | 76 | 2020 |
| 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 | 74 | 2012 |
Gianluca IaccarinoStanford University確認したメール アドレス: stanford.edu