196 citations · 677 across the 9 of their papers we have counts for
10 papers
Grouped approximate control variate estimators
Alex A. Gorodetsky, John D. Jakeman, Michael S. Eldred
This paper analyzes the approximate control variate (ACV) approach to multifidelity uncertainty quantification in the case where weighted estimators are combined to form the compon…
Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty
Friedrich Menhorn, Gianluca Geraci, D. Thomas Seidl +3
Optimization is a key tool for scientific and engineering applications, however, in the presence of models affected by uncertainty, the optimization formulation needs to be extende…
Multifidelity uncertainty quantification with models based on dissimilar parameters
Xiaoshu Zeng, Gianluca Geraci, Michael S. Eldred +3
Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the…
Multifidelity data fusion in convolutional encoder/decoder networks
Lauren Partin, Gianluca Geraci, Ahmad Rushdi +2
We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. Besides requiring si…
Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis
John D. Jakeman, Michael Eldred, Gianluca Geraci +1
In this paper, we present an adaptive algorithm to construct response surface approximations of high-fidelity models using a hierarchy of lower fidelity models. Our algorithm is ba…
Polynomial chaos expansions for dependent random variables
John Jakeman, Fabian Franzelin, Akil Narayan +2
Polynomial chaos expansions (PCE) are well-suited to quantifying uncertainty in models parameterized by independent random variables. The assumption of independence leads to simple…
A Generalized Approximate Control Variate Framework for Multifidelity Uncertainty Quantification
Alex A. Gorodetsky, Gianluca Geraci, Mike Eldred +1
We describe and analyze a variance reduction approach for Monte Carlo (MC) sampling that accelerates the estimation of statistics of computationally expensive simulation models usi…
Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations
Xun Huan, Cosmin Safta, Khachik Sargsyan +6
The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simula…
Enhancing -minimization estimates of polynomial chaos expansions using basis selection
John D. Jakeman, Michael S. Eldred, Khachik Sargsyan
In this paper we present a basis selection method that can be used with -minimization to adaptively determine the large coefficients of polynomial chaos expansions (PCE). T…
Sparse Pseudospectral Approximation Method
Paul G. Constantine, Michael S. Eldred, Eric T. Phipps
Multivariate global polynomial approximations - such as polynomial chaos or stochastic collocation methods - are now in widespread use for sensitivity analysis and uncertainty quan…