Training

There is no video for the Overview section

Duration: 45 minutes

• What is Dakota?
• Why use Dakota?
• Prerequisites

Duration: 100 minutes

• 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

Duration: 60 Minutes

• 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

Duration: 130 Minutes

• 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

Duration: 130 minutes

• 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

Duration: 90 minutes

• Sensitivity analysis goals and examples
• Global sensitivity analysis approaches and metrics available in Dakota
• Dakota examples for parameter studies and global sensitivity analysis

Duration: 50

• 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

Duration: 100

• 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

Duration: 70 minutes

• 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

Duration: 125 minutes

• Uncertainty quantification goals and examples
• Examples for uncertainty quantification
• Focus on forward propagation

Duration: 60

• 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