| A stochastic collocation algorithm with multifidelity models A Narayan, C Gittelson, D Xiu SIAM Journal on Scientific Computing 36 (2), A495-A521, 2014 | 173 | 2014 |
| Adaptive Leja sparse grid constructions for stochastic collocation and high-dimensional approximation A Narayan, JD Jakeman SIAM Journal on Scientific Computing 36 (6), A2952-A2983, 2014 | 142 | 2014 |
| A Christoffel function weighted least squares algorithm for collocation approximations A Narayan, J Jakeman, T Zhou Mathematics of Computation 86 (306), 1913-1947, 2017 | 97 | 2017 |
| Computational aspects of stochastic collocation with multifidelity models X Zhu, A Narayan, D Xiu SIAM/ASA Journal on Uncertainty Quantification 2 (1), 444-463, 2014 | 96 | 2014 |
| Polynomial chaos expansions for dependent random variables JD Jakeman, F Franzelin, A Narayan, M Eldred, D Plfüger Computer Methods in Applied Mechanics and Engineering 351, 643-666, 2019 | 94 | 2019 |
| Stochastic collocation methods on unstructured grids in high dimensions via interpolation A Narayan, D Xiu SIAM Journal on Scientific Computing 34 (3), A1729-A1752, 2012 | 85 | 2012 |
| A generalized sampling and preconditioning scheme for sparse approximation of polynomial chaos expansions JD Jakeman, A Narayan, T Zhou SIAM Journal on Scientific Computing 39 (3), A1114-A1144, 2017 | 81 | 2017 |
| Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions JD Jakeman, A Narayan, D Xiu Journal of Computational Physics 242, 790-808, 2013 | 71 | 2013 |
| Numerical integration in multiple dimensions with designed quadrature V Keshavarzzadeh, RM Kirby, A Narayan SIAM Journal on Scientific Computing 40 (4), A2033-A2061, 2018 | 62 | 2018 |
| Effectively subsampled quadratures for least squares polynomial approximations P Seshadri, A Narayan, S Mahadevan SIAM/ASA Journal on Uncertainty Quantification 5 (1), 1003-1023, 2017 | 55 | 2017 |
| Stochastic collocation on unstructured multivariate meshes A Narayan, T Zhou Communications in Computational Physics 18 (1), 1-36, 2015 | 55 | 2015 |
| 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 | 54 | 2018 |
| Flexibility reserve in power systems: Definition and stochastic multi-fidelity optimization R Khatami, M Parvania, A Narayan IEEE Transactions on Smart Grid 11 (1), 644-654, 2019 | 53 | 2019 |
| Constructing least-squares polynomial approximations L Guo, A Narayan, T Zhou SIAM Review 62 (2), 483-508, 2020 | 52 | 2020 |
| A gradient enhanced ℓ1-minimization for sparse approximation of polynomial chaos expansions L Guo, A Narayan, T Zhou Journal of Computational Physics 367, 49-64, 2018 | 52 | 2018 |
| A metalearning approach for physics-informed neural networks (PINNs): Application to parameterized PDEs M Penwarden, S Zhe, A Narayan, RM Kirby Journal of Computational Physics 477, 111912, 2023 | 42 | 2023 |
| Weighted discrete least-squares polynomial approximation using randomized quadratures T Zhou, A Narayan, D Xiu Journal of Computational Physics 298, 787-800, 2015 | 40 | 2015 |
| Multivariate discrete least-squares approximations with a new type of collocation grid T Zhou, A Narayan, Z Xu SIAM Journal on Scientific Computing 36 (5), A2401-A2422, 2014 | 40 | 2014 |
| Multifidelity modeling for physics-informed neural networks (pinns) M Penwarden, S Zhe, A Narayan, RM Kirby Journal of Computational Physics 451, 110844, 2022 | 39 | 2022 |
| RBF-LOI: Augmenting radial basis functions (RBFs) with least orthogonal interpolation (LOI) for solving PDEs on surfaces V Shankar, A Narayan, RM Kirby Journal of Computational Physics 373, 722-735, 2018 | 36 | 2018 |