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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.


For a quick installation, clone the repository and add alea/src to your PYTHONPATH.

As a developer, make sure that the unittests run smoothly. Therefore, prior to every commit, run ./bin/test_module.

We are currently preparing the release of the source code. You can already access it at alea-testing.