Engineering analysis with probability boxes: A review on computational methods
The consideration of imprecise probability in engineering analysis to account for missing,
vague or incomplete data in the description of model uncertainties is a fast-growing field of …
vague or incomplete data in the description of model uncertainties is a fast-growing field of …
Probabilistic failure mechanisms via Monte Carlo simulations of complex microstructures
A probabilistic approach to phase-field brittle and ductile fracture with random material and
geometric properties is proposed within this work. In the macroscopic failure mechanics …
geometric properties is proposed within this work. In the macroscopic failure mechanics …
Investigations on the restrictions of stochastic collocation methods for high dimensional and nonlinear engineering applications
Sophisticated sampling techniques used for solving stochastic partial differential equations
efficiently and robustly are still in a state of development. It is known in the scientific …
efficiently and robustly are still in a state of development. It is known in the scientific …
[PDF][PDF] Engineering analysis with imprecise probabilities: a state-of-the-art review on P-boxes
The consideration of imprecise probability in engineering analysis to account for missing,
vague or imcomplete data in the description of model uncertainties is a currently fast …
vague or imcomplete data in the description of model uncertainties is a currently fast …
Imprecise random field analysis with parametrized kernel functions
The application of isotropic random fields in engineering analysis requires the definition of
their first two central moments, as well as their covariance function. In general, insufficient …
their first two central moments, as well as their covariance function. In general, insufficient …
Karhunen-Loève expansion based on an analytical solution over a bounding box domain
This paper explores the accuracy and the efficiency of analytical solution of Fredholm
integral equation to represent a random field on complex geometry. Because no analytical …
integral equation to represent a random field on complex geometry. Because no analytical …
Distribution-free P-box processes based on translation theory: Definition and simulation
Typically, non-deterministic models of spatial or time dependent uncertainty are modelled
using the well-established random field framework. However, while tailored for exactly these …
using the well-established random field framework. However, while tailored for exactly these …
Interval and fuzzy physics-informed neural networks for uncertain fields
Temporally and spatially dependent uncertain parameters are regularly encountered in
engineering applications. Commonly these uncertainties are accounted for using random …
engineering applications. Commonly these uncertainties are accounted for using random …
Importance measure of probabilistic common cause failures under system hybrid uncertainty based on bayesian network
When dealing with modern complex systems, the relationship existing between components
can lead to the appearance of various dependencies between component failures, where …
can lead to the appearance of various dependencies between component failures, where …
Local explicit interval fields for non-stationary uncertainty modelling in finite element models
Interval fields have been introduced to model spatial uncertainty in Finite Element Models
when the stochastic resolution of available data is too limited to build representative …
when the stochastic resolution of available data is too limited to build representative …