General Description

The Dakota toolkit provides a flexible, extensible interface between analysis codes and iterative systems analysis methods.

Dakota contains algorithms for:

  • Optimization with gradient and nongradient-based methods
  • Uncertainty quantification with sampling, reliability, stochastic expansion, and epistemic methods
  • Parameter estimation with nonlinear least squares methods
  • 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.

Basic Usage

Sample Submission Script for Single Thread Jobs


 #$ -pe smp 8
 #$ -q long
 #$ -N job_name

 module load dakota

 dakota -i dakota_input_file.in 

Sample Submission Script for Parallel Jobs

To submit an X core Dakota job to CRC systems use the following template:


 #$ -pe mpi-8 X        # X MUST be a multiple of 8
 #$ -q debug
 #$ -N job_name

 module load dakota

 mpirun -np $NSLOTS dakota -i dakota_input_file.in 


A few examples, including job-scripts and instructions of how to run them, tested on CRC resources are available at /opt/crc/dakota/Examples. Please follow "README" file to run these examples.

Training and Manuals

  • To register for training, please visit the official Dakota training page.

Further Information

See the official site: Dakota