102 citations · 219 across the 18 of their papers we have counts for
3 papers · 1 filter
Inverse Problems in a Bayesian Setting
Hermann G. Matthies, Elmar Zander, Bojana V. Rosić +2
In a Bayesian setting, inverse problems and uncertainty quantification (UQ) --- the propagation of uncertainty through a computational (forward) model --- are strongly connected. I…
Quantification of airfoil geometry-induced aerodynamic uncertainties - comparison of approaches
Dishi Liu, Alexander Litvinenko, Claudia Schillings +1
Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods since it is computationally expensive, especially for the uncertainties caused by random…
Polynomial Chaos Expansion of random coefficients and the solution of stochastic partial differential equations in the Tensor Train format
Sergey Dolgov, Boris N. Khoromskij, Alexander Litvinenko +1
We apply the Tensor Train (TT) decomposition to construct the tensor product Polynomial Chaos Expansion (PCE) of a random field, to solve the stochastic elliptic diffusion PDE with…