ALEA - a python framework for spectral methods and low-rank approximations in uncertainty quantification. ALEA is intended as a research framework for numerical methods in Uncertainty Quantification (UQ). Its emphasis lies on: generalised polynomial chaos (gpc) methods; stochastic Galerkin FEM; adaptive numerical methods; tensor methods for UQ. Most of these areas are work in progress. The provided functionality will be extended gradually and demonstrated in related articles. The framework is written in python and uses FEniCS as its default FEM backend.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Bespalov, Alex; Silvester, David: Efficient adaptive stochastic Galerkin methods for parametric operator equations (2016)
- Silvester, David; Pranjal: An optimal solver for linear systems arising from stochastic FEM approximation of diffusion equations with random coefficients (2016)
- Eigel, Martin; Gittelson, Claude Jeffrey; Schwab, Christoph; Zander, Elmar: A convergent adaptive stochastic Galerkin finite element method with quasi-optimal spatial meshes (2015)
- Pultarová, Ivana: Adaptive algorithm for stochastic Galerkin method. (2015)
- Eigel, Martin; Gittelson, Claude Jeffrey; Schwab, Christoph; Zander, Elmar: Adaptive stochastic Galerkin FEM (2014)