ML-SGFEM

The MATLAB code in this repository (download the .zip file) is associated with the following works:

• Adam J. Crowder. Adaptive & Multilevel Stochastic Galerkin Finite Element Methods, Ph.D Thesis, Department of Mathematics, University of Manchester, 2020. https://www.research.manchester.ac.uk/portal/files/159166686/FULL_TEXT.PDF
• A.J. Crowder, C.E. Powell, and A. Bespalov. Efficient adaptive multilevel stochastic Galerkin approximation using implicit a posteriori error estimation. SIAM J. Sci. Comput., 41(3), A1681-A1705 (2019), https://doi.org/10.1137/18M1194420

and can be used with MATLAB version R2017b or later.

The code was developed by Adam Crowder, Georgios Papanikos and Catherine Powell at the University of Manchester over the period 2019-2022. Work was partially supported by the EPSRC under grant EP/V048376/1.

Instructions on installing and running the software, and a sample session for a specific test problem are provided in the User Guide which is available from: http://eprints.maths.manchester.ac.uk/2859/. It is advisable to read this document before running the code.

To cite the User Guide, please use:

• Georgios Papanikos and Catherine E. Powell, ML-SGFEM User Guide. Manchester Institute of Mathematical Sciences (MIMS) Eprint 2022.8, 2022. http://eprints.maths.manchester.ac.uk/2859/

To cite the code (current version), please use:

• Adam Crowder, Georgios Papanikos and Catherine E. Powell, ML-SGFEM Software Version 1.0, 2022. https://github.com/ceapowell/ML-SGFEM/