We overview some recent results in the field of uncertainty quantification for kinetic equations and related problems with random inputs. Uncertainties may be due to various reasons, such as lack of knowledge on the microscopic interaction details or incomplete information at the boundaries or on the initial data. These uncertainties contribute to the curse of dimensionality and the development of efficient numerical methods is a challenge. After a brief introduction on the main numerical techniques for uncertainty quantification in partial differential equations, we focus our survey on some of the recent progress on multi-fidelity methods and stochastic Galerkin methods for kinetic equation
A novel probabilistic numerical method for quantifying the uncertainty induced by the time integrati...
We study evolution equations of drift-diffusion type when various parameters are random. Motivated b...
Random differential equations arise to model smooth random phenomena. The error term, instead of bei...
This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and relate...
In this talk we will study the generalized polynomial chaos-stochastic Galerkin (gPC-SG) approach to...
In this thesis we study partial differential equations with random inputs. The effects that differen...
Kinetic equations play a major rule in modeling large systems of interacting particles. Recently the...
In this paper, we present a formal quantification of uncertainty induced by numerical solutions of o...
In this paper, we present a formal quantification of uncertainty induced by numerical solutions of o...
In this paper hyperbolic partial differential equations with random coefficients are discussed. Such...
Abstract. In this paper hyperbolic partial differential equations with random coefficients are discu...
In this paper, we present a formal quantification of epistemic uncertainty induced by numerical solu...
This book gives a comprehensive introduction to numerical methods and analysis of stochastic process...
We review uncertainty quantification (UQ) for hyperbolic systems of conservation (balance) laws. The...
Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms becau...
A novel probabilistic numerical method for quantifying the uncertainty induced by the time integrati...
We study evolution equations of drift-diffusion type when various parameters are random. Motivated b...
Random differential equations arise to model smooth random phenomena. The error term, instead of bei...
This book explores recent advances in uncertainty quantification for hyperbolic, kinetic, and relate...
In this talk we will study the generalized polynomial chaos-stochastic Galerkin (gPC-SG) approach to...
In this thesis we study partial differential equations with random inputs. The effects that differen...
Kinetic equations play a major rule in modeling large systems of interacting particles. Recently the...
In this paper, we present a formal quantification of uncertainty induced by numerical solutions of o...
In this paper, we present a formal quantification of uncertainty induced by numerical solutions of o...
In this paper hyperbolic partial differential equations with random coefficients are discussed. Such...
Abstract. In this paper hyperbolic partial differential equations with random coefficients are discu...
In this paper, we present a formal quantification of epistemic uncertainty induced by numerical solu...
This book gives a comprehensive introduction to numerical methods and analysis of stochastic process...
We review uncertainty quantification (UQ) for hyperbolic systems of conservation (balance) laws. The...
Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms becau...
A novel probabilistic numerical method for quantifying the uncertainty induced by the time integrati...
We study evolution equations of drift-diffusion type when various parameters are random. Motivated b...
Random differential equations arise to model smooth random phenomena. The error term, instead of bei...