Short Course: Uncertainty Quantification in Physics-Based Modeling Using Python

 Dimitris Tsapetis, Postdoctoral Fellow – JHU, Dimitris Giovianis, Assistant Research Professor – JHU, and Michael Shields, Associate Professor – JHU , held a three-day short course at Johns Hopkins University to introduce the practice of uncertainty quantification (UQ) in computational modeling of physical systems using the Python programming language. The course included a morning session of instruction followed by an afternoon session of hands-on learning, and was attended by members of the URA, DTRA, and the GCAP. Participants were introduced to a variety of UQ tasks including uncertainty propagation, surrogate modeling, and Bayesian inference using the UQpy toolbox which empowers participants to begin applying UQ to computational models in their respective fields of study.