Help Resources
Public User Community
Dakota is an open source and publicly available project with a significant worldwide user base. Our vision is that Dakota users will increasingly help each other with questions and issues. We especially encourage general usage questions about how to use Dakota, including how to install, choose algorithms, or interface to an application code, but all Dakota topics are welcome.
Dakota Github Discussions
Vist Dakota’s GitHub Discussions page for questions about installing, building, and using Dakota, sharing Dakota success stories, reporting bugs, and making feature requests.
If you don’t already have a GitHub account, you’ll need to create one to participate.
Screencasts
In addition to this manual, the Dakota team has developed a series of video screencasts that are aimed at helping users learn how to use Dakota at varying stages of complexity. These screencasts complement the material presented in the Dakota GUI manual, since many of the videos use the GUI to demonstrate Dakota usage.
Series 1: Introduction to Dakota
Series 2: Sensitivity Analysis
2.3: Variance-Based Decomposition (coming soon)
Training Resources
Slides and streaming videos for several introductory Dakota training topics are now available. The videos are recordings of live training conducted internally at Sandia and feature:
Slide presentations and lectures by Dakota team members
Live demos of Dakota
Interaction with trainees
Dakota exercises that provide hands-on experience with using the Reference Manual, creating input files, interfacing simulations with Dakota, interpreting Dakota output, and more
Viewers may follow along with the exercises by downloading the materials for each module. The exercises were created for use with Dakota 6.3 on OS X or Linux, but users of slightly different versions of Dakota (6.0 or greater) and Windows users of Dakota may encounter a few difficulties.
Updated (2016) materials and presentations can be downloaded here, but they may not match those used in the videos.
Note
Exercises in the Model Characterization and Sensitivity Analysis modules make use of plotting tools created specially for the training. Python 2.7 and the matplotlib and pandas libraries are required by the tools. Installing either Anaconda or Canopy is a convenient way to satisfy these requirements.
Warning
Cantilever beam errata: In the content below (slides and simulation drivers), the stress equation for the cantilever beam incorrectly has a fixed length L = 100. Corrected cantilever beam slide. The analysis driver is corrected in versions of Dakota newer than 2019-09-05. Thanks to Anjali Sandip for reporting.
Module |
Learning Goals |
Approx. Time (minutes) |
Video/Slides/Exercises |
---|---|---|---|
Overview |
|
45 |
|
Model Characterization |
|
100 |
|
Input Syntax / Building Blocks |
|
60 |
|
Interfacing a User’s Simulation to Dakota |
|
130 |
|
Sensitivity Analysis |
|
90 |
|
Surrogate Models |
|
50 |
|
Optimization |
|
100 |
|
Calibration |
|
70 |
|
Uncertainty Quantification |
|
125 |
|
Parallel Options |
|
60 |