A technique for coupling an intrusive and non-intrusive uncertainty quantification method is proposed. The intrusive approach uses a combination of polynomial chaos and stochastic Galerkin projection. The non-intrusive method uses numerical integration by combining quadrature rules and the probability density functions of the prescribed uncertainties. A stable coupling procedure between the two methods at an interface is constructed. The efficiency of the hybrid method is exemplified using hyperbolic systems of equations, and verified by numerical experiments.Funding; Linkoping University</p
This paper examines uncertainty quantification in computational fluid dynamics (CFD) with non-intrus...
Recent advances in the field of uncertainty quantification are based on achieving suitable functiona...
The paper describes the philosophy, design, functionality, and usage of the Python software toolbox ...
A technique for coupling an intrusive and non-intrusive uncertainty quantification method is propose...
We consider Uncertainty Quantification (UQ) by expanding the solution in so-called generalized Polyn...
In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in...
The polynomial chaos method has been widely adopted as a compu-tationally feasible approach for unce...
This report describes a stochastic collocation method to adequately handle a physically intrinsic un...
The purpose of this paper is to provide a description and a comparison of a method already described...
Inherent physical uncertainties can have a significant influence on computational predictions. It is...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
A novel probabilistic numerical method for quantifying the uncertainty induced by the time integrati...
The uncertainties can generate fluctuations with aerodynamic characteristics. Uncertainty Quantifica...
International audienceIn this chapter, the basic principles of two methodologies for uncertainty qua...
This paper presents a methodology to quantify computationally the uncertainty in a class of differen...
This paper examines uncertainty quantification in computational fluid dynamics (CFD) with non-intrus...
Recent advances in the field of uncertainty quantification are based on achieving suitable functiona...
The paper describes the philosophy, design, functionality, and usage of the Python software toolbox ...
A technique for coupling an intrusive and non-intrusive uncertainty quantification method is propose...
We consider Uncertainty Quantification (UQ) by expanding the solution in so-called generalized Polyn...
In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in...
The polynomial chaos method has been widely adopted as a compu-tationally feasible approach for unce...
This report describes a stochastic collocation method to adequately handle a physically intrinsic un...
The purpose of this paper is to provide a description and a comparison of a method already described...
Inherent physical uncertainties can have a significant influence on computational predictions. It is...
Uncertainty quantification seeks to provide a quantitative means to understand complex systems that ...
A novel probabilistic numerical method for quantifying the uncertainty induced by the time integrati...
The uncertainties can generate fluctuations with aerodynamic characteristics. Uncertainty Quantifica...
International audienceIn this chapter, the basic principles of two methodologies for uncertainty qua...
This paper presents a methodology to quantify computationally the uncertainty in a class of differen...
This paper examines uncertainty quantification in computational fluid dynamics (CFD) with non-intrus...
Recent advances in the field of uncertainty quantification are based on achieving suitable functiona...
The paper describes the philosophy, design, functionality, and usage of the Python software toolbox ...