Dakota
Software for black-box, ensemble analysis of computationally costly simulations
Area: Mathematical libraries
CASS member: FASTMath
Description
The Dakota project delivers both state-of-the-art research and robust, usable software for optimization and UQ. Broadly, the Dakota software’s advanced parametric analyses enable design exploration, model calibration, risk analysis, and quantification of margins and uncertainty with computational models. The Dakota toolkit provides a flexible, extensible interface between such simulation codes and its iterative systems analysis methods, which include optimization (gradient and derivative-free methods), uncertainty quantification (sampling, reliability, stochastic expansion, and epistemic methods), parameter estimation (deterministic with nonlinear least squares and stochastic with Bayesian inference), and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty.
Target audience
Dakota is for engineers, scientists, and decision-makers who wish to perform iterative analyses for optimization, uncertainty quantification, senstivity analysis, or model calibration on their computational simulations. Compatible with Linux, macOS, and Windows, it runs on systems of all scales from laptops to HPCs, and can be used as a C++ or Python library, command-line executable, and via a GUI.
License: LGPL-2.1-only