Uncertainty quantification for hybrid random logistic models with harvesting via density functions

2022 ◽  
Vol 155 ◽  
pp. 111762
Author(s):  
J.-C. Cortés ◽  
A. Moscardó-García ◽  
R.-J. Villanueva
Keyword(s):  
Uncertainty Quantification ◽  
Logistic Models ◽  
Density Functions

scholarly journals On Stochastic Investigation of Flow Problems Using the Viscous Burgers’ Equation as an Example

2019 ◽  
Vol 81 (2) ◽  
pp. 1111-1117 ◽  
Author(s):  
Markus Wahlsten ◽  
Jan Nordström
Keyword(s):  
Uncertainty Quantification ◽  
Polynomial Chaos ◽  
Burgers Equation ◽  
Probability Density Functions ◽  
Galerkin Projection ◽  
Density Functions ◽  
Flow Problems ◽  
Stochastic Space ◽  
Stochastic Galerkin ◽  
Insight Into

Abstract We consider a stochastic analysis of non-linear viscous fluid flow problems with smooth and sharp gradients in stochastic space. As a representative example we consider the viscous Burgers’ equation and compare two typical intrusive and non-intrusive uncertainty quantification methods. The specific intrusive approach uses a combination of polynomial chaos and stochastic Galerkin projection. The specific non-intrusive method uses numerical integration by combining quadrature rules and the probability density functions of the prescribed uncertainties. The two methods are compared in terms of error in the estimated variance, computational efficiency and accuracy. This comparison, although not general, provide insight into uncertainty quantification of problems with a combination of sharp and smooth variations in stochastic space. It suggests that combining intrusive and non-intrusive methods could be advantageous.

INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

2020 ◽  
Vol 10 (4) ◽  
pp. 333-349
Author(s):  
Kevin de Vries ◽  
Anna Nikishova ◽  
Benjamin Czaja ◽  
Gábor Závodszky ◽  
Alfons G. Hoekstra
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Cell Model ◽  
A Cell
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