Dakota Training Materials

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 will encounter few difficulties.

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.

The most up-to-date materials and presentations can be downloaded here, but they may not match those used in the videos.

Module Learning Goals Approximate Time (minutes) Video/Slides/Exercises
Overview
  • What is Dakota?
  • Why use Dakota?
  • Prerequisites
 45 Slides
Model Characterization
  • See how Dakota can automate what you are already doing
  • Know what model characteristics will affect how you use Dakota
  • Be able to run a basic study to characterize a model
 100 Video/Slides/Exercises
Input Syntax / Building Blocks
  • Develop an accurate “mental model” of Dakota components
  • Understand how to configure Dakota components using a Dakota input file
  • Become familiar with the Dakota Reference Manual
 60 Video/Slides/Exercises
Interfacing a User's Simulation to Dakota
  • Mechanics of how Dakota communicates with and runs a simulation
  • Requirements this places on the user and interface
  • Basic strategies for developing a simulation interface
  • Convenience features Dakota provides for managing simulation runs
  • Note: This module covers "black box" interfacing, not "library mode" Dakota
 130 Video 1/Video 2/Slides/Exercises
Sensitivity Analysis
  • Sensitivity analysis goals and examples
  • Global sensitivity analysis approaches and metrics available in Dakota
  • Dakota examples for parameter studies and global sensitivity analysis
 90 Video/Slides/Exercises
Surrogate Models
  • Define a surrogate model
  • Identify situations where it may be appropriate to use a surrogate model
  • Learn how to specify a surrogate model in Dakota
  • Run a surrogate model in Dakota and examine outputs based on the surrogate model
  • Identify some common diagnostics for surrogates
  • Understand different ways surrogates are used in Dakota
 50 Video/Slides/Exercises
Optimization
  • Understand potential goals of optimization and optimization terminology
  • Learn how to communicate the relevant problem information to Dakota
  • Become familiar with several types of optimization solvers and how to choose from among them based on problem type and goals
 100 Video/Slides/Exercises
Calibration
  • Why you might want to tune models to match data via calibration (parameter estimation)
  • How to formulate calibration problems and present them to Dakota
  • What Dakota methods can help you achieve calibration goals
 70 Video/Slides/Exercises
Uncertainty Quantification
  • Uncertainty quantification goals and examples
  • Examples for uncertainty quantification
  • Focus on forward propogation
 125 Video/Slides/Exercises
Parallel Options
  • Discuss what to consider when designing a parallelized study
  • Understand what Dakota provides and its limitations
  • Be able to choose the best parallelism approach
  • Know how to configure Dakota and your interface for your parallelism approach
 60 Video/Slides