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:
- optimization with gradient and nongradient-based methods;
- uncertainty quantification with sampling, reliability, stochastic expansion, and epistemic methods;
- parameter estimation using nonlinear least squares (deterministic) or Bayesian inference (stochastic); 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. ¹
This version is installed and currently available on environment hpc-env/6.4:
If you want to find out more about dakota on the HPC cluster, you can use the command
module spider dakota
This will show you basic informations e.g. a short description and the currently installed version.
To load the desired version of the module, use the command, e.g.
module load hpc-env 6.4 module load dakota
Always remember: this command is case sensitive!
To get an overview on how to use dakota, you can use the help function: