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Akil Narayan
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A stochastic collocation algorithm with multifidelity models
A Narayan, C Gittelson, D Xiu
SIAM Journal on Scientific Computing 36 (2), A495-A521, 2014
1742014
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
1422014
A Christoffel function weighted least squares algorithm for collocation approximations
A Narayan, J Jakeman, T Zhou
Mathematics of Computation 86 (306), 1913-1947, 2017
972017
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
972014
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
962019
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
852012
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
812017
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
712013
Numerical integration in multiple dimensions with designed quadrature
V Keshavarzzadeh, RM Kirby, A Narayan
SIAM Journal on Scientific Computing 40 (4), A2033-A2061, 2018
622018
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
562017
Stochastic collocation on unstructured multivariate meshes
A Narayan, T Zhou
Communications in Computational Physics 18 (1), 1-36, 2015
552015
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
542019
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
542018
Constructing least-squares polynomial approximations
L Guo, A Narayan, T Zhou
SIAM Review 62 (2), 483-508, 2020
532020
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
532018
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
462023
Multifidelity modeling for physics-informed neural networks (pinns)
M Penwarden, S Zhe, A Narayan, RM Kirby
Journal of Computational Physics 451, 110844, 2022
412022
Weighted discrete least-squares polynomial approximation using randomized quadratures
T Zhou, A Narayan, D Xiu
Journal of Computational Physics 298, 787-800, 2015
402015
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
392014
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
362018
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