36 citations · 82 across the 6 of their papers we have counts for
6 papers
Kriging in Tensor Train data format
Sergey Dolgov, Alexander Litvinenko, Dishi Liu
Combination of low-tensor rank techniques and the Fast Fourier transform (FFT) based methods had turned out to be prominent in accelerating various statistical operations such as K…
FFT-based homogenisation accelerated by low-rank tensor approximations
Jaroslav Vondřejc, Dishi Liu, Martin Ladecký +1
Fast Fourier transform (FFT) based methods have turned out to be an effective computational approach for numerical homogenisation. In particular, Fourier-Galerkin methods are compu…
Pivoted Cholesky decomposition by Cross Approximation for efficient solution of kernel systems
Dishi Liu, Hermann G. Matthies
Large kernel systems are prone to be ill-conditioned. Pivoted Cholesky decomposition (PCD) render a stable and efficient solution to the systems without a perturbation of regulariz…
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…
To be or not to be intrusive? The solution of parametric and stochastic equations --- Proper Generalized Decomposition
Loïc Giraldi, Dishi Liu, Hermann G. Matthies +1
A numerical method is proposed to compute a low-rank Galerkin approximation to the solution of a parametric or stochastic equation in a non-intrusive fashion. The considered nonlin…
To be or not to be intrusive? The solution of parametric and stochastic equations - the "plain vanilla" Galerkin case
Loïc Giraldi, Alexander Litvinenko, Dishi Liu +2
In parametric equations - stochastic equations are a special case - one may want to approximate the solution such that it is easy to evaluate its dependence of the parameters. Inte…