A stochastic collocation algorithm with multifidelity models A Narayan, C Gittelson, D Xiu SIAM Journal on Scientific Computing 36 (2), A495-A521, 2014 | 169 | 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 | 141 | 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 | 96 | 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 | 94 | 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 | 91 | 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 | 80 | 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 | 69 | 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 | 61 | 2018 |

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 |

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 | 54 | 2017 |

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 | 50 | 2018 |

Constructing least-squares polynomial approximations L Guo, A Narayan, T Zhou SIAM Review 62 (2), 483-508, 2020 | 49 | 2020 |

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 | 45 | 2019 |

Weighted discrete least-squares polynomial approximation using randomized quadratures T Zhou, A Narayan, D Xiu Journal of Computational Physics 298, 787-800, 2015 | 39 | 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 | 39 | 2014 |

Multifidelity modeling for physics-informed neural networks (pinns) M Penwarden, S Zhe, A Narayan, RM Kirby Journal of Computational Physics 451, 110844, 2022 | 37 | 2022 |

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 | 36 | 2023 |

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 |