Combustion kinetic model uncertainty quantification, propagation and minimization
The current interest in the combustion chemistry of hydrocarbon fuels, including the various
alcohol and biodiesel compounds, motivates this review of the methods and application of …
alcohol and biodiesel compounds, motivates this review of the methods and application of …
Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics
HN Najm - Annual review of fluid mechanics, 2009 - annualreviews.org
The quantification of uncertainty in computational fluid dynamics (CFD) predictions is both a
significant challenge and an important goal. Probabilistic uncertainty quantification (UQ) …
significant challenge and an important goal. Probabilistic uncertainty quantification (UQ) …
[BUKU][B] Uncertainty quantification
C Soize - 2017 - Springer
This book results from a course developed by the author and reflects both his own and
collaborative research regarding the development and implementation of uncertainty …
collaborative research regarding the development and implementation of uncertainty …
[HTML][HTML] Chaospy: An open source tool for designing methods of uncertainty quantification
J Feinberg, HP Langtangen - Journal of Computational Science, 2015 - Elsevier
The paper describes the philosophy, design, functionality, and usage of the Python software
toolbox Chaospy for performing uncertainty quantification via polynomial chaos expansions …
toolbox Chaospy for performing uncertainty quantification via polynomial chaos expansions …
Simulation-based optimal Bayesian experimental design for nonlinear systems
The optimal selection of experimental conditions is essential to maximizing the value of data
for inference and prediction, particularly in situations where experiments are time …
for inference and prediction, particularly in situations where experiments are time …
On the convergence of generalized polynomial chaos expansions
OG Ernst, A Mugler, HJ Starkloff… - … Modelling and Numerical …, 2012 - esaim-m2an.org
A number of approaches for discretizing partial differential equations with random data are
based on generalized polynomial chaos expansions of random variables. These constitute …
based on generalized polynomial chaos expansions of random variables. These constitute …
Digital twin concepts with uncertainty for nuclear power applications
Digital Twins (DTs) are receiving considerable attention from multiple disciplines. Much of
the literature at this time is dedicated to the conceptualization of digital twins, and associated …
the literature at this time is dedicated to the conceptualization of digital twins, and associated …
[BUKU][B] Scientific Computation
P Joly, A Quarteroni, J Rappaz - 2005 - Springer
Two decades ago when we wrote Spectral Methods in Fluid Dynamics (1988), the subject
was still fairly novel. Motivated by the many favorable comments we have received and the …
was still fairly novel. Motivated by the many favorable comments we have received and the …
Stochastic spectral methods for efficient Bayesian solution of inverse problems
We present a reformulation of the Bayesian approach to inverse problems, that seeks to
accelerate Bayesian inference by using polynomial chaos (PC) expansions to represent …
accelerate Bayesian inference by using polynomial chaos (PC) expansions to represent …
Stochastic response surface method for reliability analysis of rock slopes involving correlated non-normal variables
This paper proposes a stochastic response surface method for reliability analysis involving
correlated non-normal random variables, in which the Nataf transformation is adopted to …
correlated non-normal random variables, in which the Nataf transformation is adopted to …