MUQ is a library for inverse and forward uncertainty quantification.
Our goal is to provide an easy-to-use framework for defining and solving UQ problems with complex models in c++ and Python.
MCMC
Sampling with advanced MCMC techniques including adaptive, multi-level, and dimension independent methods.
Modeling
Advanced tools for combining model components and computing gradients and Hessian-actions.
Gaussian Processes
Regression and prior modeling using GPs, with support for derivative observations and Karhunen-Loeve expansions.
Polynomial Chaos
Support for pseudospectral construction of PCEs, forward UQ, and global sensitivity analysis.
MUQ is on Slack! - Join our slack workspace to connect with other users, get help, and discuss new features.
This material is based upon work supported by the National Science Foundation under Grant No. 1550487.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
This material is based upon work supported by the US Department of Energy, Office of Advanced Scientific Computing Research, SciDAC (Scientific Discovery through Advanced Computing) program under awards DE-SC0007099 and DE-SC0021226, for the QUEST and FASTMath SciDAC Institutes.