MSEE: Short Course on Uncertainty Quantification
Dimitris Giovanis, Assistant Research Professor, Johns Hopkins University
Michael D. Shields, Associate Professor, Johns Hopkins University
Please register here by May 15, 2021.
The course will be held on June 2-4. Each day will be broken into two sessions that will run from 11AM-1PM and 2-4PM EDT.
This three-day course introduces the fundamental concepts of uncertainty quantification (UQ) and
propagation in complex multiscale engineering systems. Each day is broken into two sessions. In the
first session UQ methodology is presented. This is followed by Python modeling exercises using the
UQpy software in the second session. At the end of this course, it is the goal that attendees will have
a foundation in the principles of UQ and will begin developing the practical skills to apply these
principles to 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
- Apply Bayesian techniques for inferring uncertainty from various data sources.
- Participants should have an 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.
- 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.
Dr. Giovanis is an Assistant Research Professor in the Dept. of Civil and Systems Engineering at Johns Hopkins. His research interests lie in the area of data-driven uncertainty quantification (UQ) approaches and reliability analysis using machine learning.
Dr. Shields is an Associate Professor in the Dept. of Civil and Systems Engineering at Johns Hopkins. Dr. Shields’s research focuses on uncertainty quantification for wide-ranging problems in computational mechanics and materials science.