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| |__ |_ _| _ \| _ \ \ / / (_) |__ | \/ | | | |/ _ \
| '_ \ | || |_) | |_) \ V /| | | '_ \ _____| |\/| | | | | | | |
| | | || || __/| __/ | | | | | |_) |_____| | | | |_| | |_| |
|_| |_|___|_| |_| |_| |_|_|_.__/ |_| |_|\___/ \__\_\
Scalable Markov chain Monte Carlo Sampling Methods for Large-scale Bayesian Inverse Problems Governed by PDEs
hIPPYlib-MUQ is a Python interface between two open source softwares, hIPPYlib
and MUQ, which have complementary capabilities. hIPPYlib is an extensible
software package aimed at solving deterministic and linearized Bayesian inverse
problems governed by PDEs.
MUQ is a collection of tools for solving uncertainty quantification problems.
hIPPYlib-MUQ integrates these two libraries into a unique software framework,
allowing users to implement the state-of-the-art Bayesian inversion algorithms
in a seamless way.
To get started, we recommend to follow the interactive tutorial in tutorial
folder, which provides step-by-step implementations by solving an example
problem.
A static version of the tutorial is also available here.
hIPPYlib-MUQ is the interface program between hIPPYlib and MUQ, which
should be, of course, installed first.
We highly recommend to use our prebuilt Docker image, which is the easiest way
to run hIPPYlib-MUQ. With Docker installed on your
system, type:
docker run -ti --rm -p 8888:8888 ktkimyu/hippylib2muq 'jupyter-notebook --ip=0.0.0.0'
The notebook will be available at the following address in your web-browser.
From there you can run your own interactive notebooks or the tutorial notebook in
tutorial folder.
See INSTALL for further details.
A complete API documentation of hIPPYlib-MUQ is available
here.
- Ki-Tae Kim, University of California, Merced
- Umberto Villa, Washington University in St. Louis
- Matthew Parno, Dartmouth College
- Noemi Petra, University of California, Merced
- Youssef Marzouk, Massachusetts Institute of Technology
- Omar Ghattas, The University of Texas at Austin