MIT Uncertainty Quantification (MUQ) Library

In a nutshell, MUQ is a collection of tools for constructing models and a collection of UQ algorithms for working on those models. Our goal is to provide an easy and clean way to set up and efficiently solve uncertainty quantification problems.

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GPEXP: Experimental Design for Gaussian Process Regression in Python

GPEXP is a software package, written in python2.7, for performing experimental design in the context of GP regression. Experimental design may be performed for a variety of cost function specifications. Currently supported cost functions include those based on integrated variance, conditional entropy, and mutual information. GPEXP may also be used for general purpose GP regression. Currently supported kernels include the isotropic and anisotropic squared exponential kernel, the isotropic Matern kernel, and the Mehler kernel. Additional kernels may be easily specified. GPEXP also includes optimization routines for estimating kernel hyperparameters from data.

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NOWPAC (Nonlinear Optimization With Path-Augmented Constraints)

NOWPAC is a software package for derivative-free nonlinear constrained local optimization. The code is based on a trust region framework using surrogates of minimum Frobenius norm type for the objective function and the constraints. The code does not require gradient information and is designed to work with only black-box evaluations of the objective function and the constraints. In addition to the optimization procedure, NOWPAC provides a noise detection tool which identifies inaccurate black-box evaluations that might corrupt the optimal result or prevent the optimization procedure from making further progress.

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Feb 1, 2017
Congratulations to Ricardo Baptista and Ben Zhang for passing their PhD qualifying exams!

Sep 2, 2016
Congratulations to Alex Gorodetsky for successfully defending his PhD thesis!

July 2016
Congratulations to Tiangang Cui for accepting a tenure-track position at Monash University, in the School of Mathematical Sciences.

Feb 27, 2016
Congratulations to Alessio Spantini for winning the student paper competition at the 2016 Copper Mountain Meeting on Iterative Methods!

Feb 20, 2016
Congratulations to Alex Gorodetsky for his selection as the next John von Neumann Fellow in Computational Science at Sandia National Laboratories.

Jan 31, 2016
Congratulations to Antoni Musolas for passing his PhD qualifying exam!

Dec 1, 2015
Congratulations to Nikhil Galagali for successfully defending his PhD thesis!

Aug 1, 2015
Congratulations to Xun Huan for successfully defending his PhD thesis!

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