This three-day course introduces the basics concepts of uncertainty quantification and propagation in complex multiscale engineering systems. Probabilistic and Uncertainty Quantification (UQ) methods are presented in the morning followed by Python modeling exercises using the UQpy software in the afternoon to reinforce the material. At the end of this course, it is the goal that attendees will have a foundation in the principles of UQ and the practical acumen to apply these principles to UQ problems in their application areas of interest.
More specifically, attendees will learn how to:
- Identify source of uncertainties in models and data
- Represent uncertainties in model inputs and outputs and experimental data sources
- Select and apply methods to propagate uncertainties in computational models with an eye on computational efficiency.
- Apply Bayesian techniques for inferring uncertainty from various data sources.
Pre-Workshop Activities
- Participants should have a basic (undergraduate-level) knowledge of probability and statistics. Probability theory will not be presented in general, only specific components that are necessary will be presented but will require some prerequisite knowledge. Some materials can be developed for pre-workshop study if needed.
- Participants should have Python installed on their system and have at least a beginning knowledge of how to code in Python
- Participants will be given instructions for installing the open source software UQpy and any other necessary software on their system.
- If the participant has a simple code that they would like to use for UQ, they are encouraged to have an example available. Instructions will be provided.