Dakota: A Toolkit for Sensitivity Analysis, Uncertainty Quantification, and Calibration
Dakota is an open-source toolkit with several types of algorithms, including sensitivity analysis (SA), uncertainty quantification (UQ), optimization, and parameter calibration. Dakota provides a flexible, extensible interface between computational simulation codes and iterative analysis methods such as UQ and SA methods. Dakota has been designed to run on high-performance computing platforms and handles a variety of parallelism. In this clinic, we will provide an overview of Dakota algorithms, specifically focusing on uncertainty quantification (including various types of sampling, reliability analysis, stochastic expansion, and epistemic methods), sensitivity analysis (including variance-based decomposition methods and design of experiments), and parameter calibration (including nonlinear least squares and Bayesian methods). The tutorial will provide an overview of the methods and discuss how to use them. In addition, we will briefly cover how to interface your simulation code to Dakota.