allows analyses of systems with low input correlations without suffering its restriction ty acr ende r wh of realizations. Generalized polynomial chaos (gPC) [15–17] is a more recent approach to quantifying uncertainty within system models. It involves expressing stochastic quantities as orthogonal polynomials of the random input parameters; various orthogonal polynomials can be chosen to achieve better convergence. The gPC expansion is essentially a spectral representation i
Polynomial chaos expansions (PCE) are an attractive technique for uncertainty quan-tification (UQ) d...
Uncertainty is a common feature in first-principles models that are widely used in various engineeri...
Time delay is ubiquitous in many real-world physical and biological systems. It typically gives rise...
In many fields, active research is currently focused on quantification and simulation of model uncer...
Abstract. In this paper we review some applications of generalized polynomial chaos expansion for un...
This paper presents a methodology to quantify computationally the uncertainty in a class of differen...
Inherent physical uncertainties can have a significant influence on computational predictions. It is...
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and ...
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and ...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
peer reviewedStochastic spectral methods are widely used in uncertainty propagation thanks to its ab...
Here, we examine the suitability of truncated Polynomial Chaos Expansions (PCE) and truncated Gram-C...
This study applies generalized polynomial chaos theory to dynamic systems with uncertainties
We consider Uncertainty Quanti¿cation (UQ) by expanding the solution in so-called generalized Polyno...
Uncertainty quantification techniques based on the spectral approach have been studied extensively i...
Polynomial chaos expansions (PCE) are an attractive technique for uncertainty quan-tification (UQ) d...
Uncertainty is a common feature in first-principles models that are widely used in various engineeri...
Time delay is ubiquitous in many real-world physical and biological systems. It typically gives rise...
In many fields, active research is currently focused on quantification and simulation of model uncer...
Abstract. In this paper we review some applications of generalized polynomial chaos expansion for un...
This paper presents a methodology to quantify computationally the uncertainty in a class of differen...
Inherent physical uncertainties can have a significant influence on computational predictions. It is...
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and ...
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and ...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
peer reviewedStochastic spectral methods are widely used in uncertainty propagation thanks to its ab...
Here, we examine the suitability of truncated Polynomial Chaos Expansions (PCE) and truncated Gram-C...
This study applies generalized polynomial chaos theory to dynamic systems with uncertainties
We consider Uncertainty Quanti¿cation (UQ) by expanding the solution in so-called generalized Polyno...
Uncertainty quantification techniques based on the spectral approach have been studied extensively i...
Polynomial chaos expansions (PCE) are an attractive technique for uncertainty quan-tification (UQ) d...
Uncertainty is a common feature in first-principles models that are widely used in various engineeri...
Time delay is ubiquitous in many real-world physical and biological systems. It typically gives rise...
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