Cited by

• 2012Multifidelity Uncertainty Quantification Using Non-Intrusive Polynomial Chaos and Stochastic Collocation162 cited
• 2021Sensitivity analysis: A discipline coming of age87 cited
• 2017Probabilistic nonconvex constrained optimization with fixed number of function evaluations28 cited
• 2019A framework for global reliability sensitivity analysis in the presence of multi-uncertainty27 cited
• 2016Error correction in multi-fidelity molecular dynamics simulations using functional uncertainty quantification25 cited
• 2023PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling19 cited
• 2020PolyChaos.jl — A Julia Package for Polynomial Chaos in Systems and Control18 cited
• 2019Performance of non-intrusive uncertainty quantification in the aeroservoelastic simulation of wind turbines16 cited
• 2023Uncertainty Quantification Analysis of Exhaust Gas Plume in a Crosswind15 cited
• 2015Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models15 cited
• 2022Uncertainty Quantification of Material Properties in Ballistic Impact of Magnesium Alloys14 cited
• 2017Dakota: Bridging Advanced Scalable Uncertainty Quantification Algorithms with Production Deployment11 cited
• 2017High‐dimensional uncertainty quantification for an electrothermal field problem using stochastic collocation on sparse grids and tensor train decompositions10 cited
• 2017Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models10 cited
• 2016POF-Darts: Geometric adaptive sampling for probability of failure8 cited
• 2019Uncertainty Quantification of a Rotorcraft Conceptual Sizing Toolsuite8 cited