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.
Sample Submission Script for Single Thread Jobs
#!/bin/csh #$ -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:
#!/bin/csh #$ -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.
See the official site: Dakota