Software

Python module implementing horsetail matching: a method for optimization under uncertainty that matches the CDF of a quantity of interest to a target using efficient gradient-based optimization algorithms.

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Tutorials for this module are given in the following python notebooks:

Introduction

Mixed Uncertainties

Gradients

Targets

Surrogates

Full Example

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Documentation of the python module can be found here: Documentation

Dominance Optimizers

Python module of multi-objective optimization algorithms that can make use of multiple dominance criteria for optimization under uncertainty. Examples include a multi-objective genetic algorithm and a multi-objective Tabu search.

This python module is provided to recreate results from the publication "Using Multiple Dominance Criteria in Multi-Objective Optimizers for Aerospace Design Under Uncertainty"

Generalized Information Reuse

Python module implementing a generalized version of information reuse: a multi-fidelity method for optimization under uncertainty.

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This module is provided to recreate results from the publication "Generalized Information Reuse for Optimization Under Uncertainty With Non-sample Average Estimators".