PyMLMC is a highly modular Python Multi-Level Monte Carlo (MLMC) software
targeted at launching and managing Uncertainty Quantification campaigns
of deterministic HPC simulation software on super-computers and post-processing the results.
Installation
To obtain a local copy of the repository, execute in terminal:
git clone https://github.com/cselab/PyMLMC.git
Afterwards, copy the appropriate machine configuration file from 'cfg/' to the root of the repository and rename it to 'local.py'
Examples
For example script using the PyMLMC framework, check 'doc/examples/', in particular 'example_deterministic.py' and 'example_mlmc.py'.
Acknowledgements
If you are using this code (modified or unmodified), please cite:
J. Šukys, U. Rasthofer, F. Wermelinger, P. Hadjidoukas, P.Koumoutsakos.
"Uncertainty quantification in multi-phase cloud cavitation collapse flows using optimal control variate multi-level Monte Carlo sampling".
In progress, 2016.
Support
This code was developed in ETH Zurich, Switzerland.
Contact: Jonas Sukys, jonas.sukys@mavt.ethz.ch.