standardgalactic / hippylib2muq

Scalable Markov chain Monte Carlo Sampling Methods for Large-scale Bayesian Inverse Problems Governed by PDEs

 _     ___ ____  ______   ___ _ _           __  __ _   _  ___  
| |__ |_ _|  _ \|  _ \ \ / / (_) |__       |  \/  | | | |/ _ \ 
| '_ \ | || |_) | |_) \ V /| | | '_ \ _____| |\/| | | | | | | |
| | | || ||  __/|  __/ | | | | | |_) |_____| |  | | |_| | |_| |
|_| |_|___|_|   |_|    |_| |_|_|_.__/      |_|  |_|\___/ \__\_\
                                                               

Scalable Markov chain Monte Carlo Sampling Methods for Large-scale Bayesian Inverse Problems Governed by PDEs

Overview

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.

Installation

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.

Documentation

A complete API documentation of hIPPYlib-MUQ is available here.

Authors

• Ki-Tae Kim, University of California, Merced
• Umberto Villa, Washington University in St. Louis
• Matthew Parno, The United States Army Corps of Engineers
• Noemi Petra, University of California, Merced
• Youssef Marzouk, Massachusetts Institute of Technology
• Omar Ghattas, The University of Texas at Austin

License

GPL3