Uncertainty Quantification (UQ)

UQ lab’s mission is to accelerate scientific discoveries and enhance decision-making under uncertainty using novel computational tools based on a deep integration of computation, mathematical modeling, and experimentation to generate a detailed understanding of the phenomenon of interest.

The central challenge in using computational models for scientific discovery, engineering design, or decision support is that the process follows a path contaminated with errors and uncertainties. The inherent uncertainties are the result of many factors: experimental uncertainties, model structure inadequacies, uncertainties in model parameters and initial conditions, as well as errors due to numerical discretizations.

UQ is the theoretical and computational fabric that connects the three pillars of science – theory, experimentation, and computation – through which uncertainties are characterized and informed to guide the scientific discovery and decision-making process.

Our underlying philosophy is that the building blocks of UQ (model calibration, validation, uncertainty propagation, model refinement and experimental design) are closely related and they cannot be studied in isolation. The development of methodologies and algorithms for any of these problems need to be informed by this natural relationship to fulfill the promise of modeling and simulation in accelerating scientific discoveries and decision-making under uncertainty.

The UQ research activities in our group fall into two categories: development of UQ methodologies and algorithms and UQ applications via funded collaborations with researchers in various sciences and engineering disciplines. Every project is a mixture between methodology development and application, and as such, every graduate student in the UQ lab is exposed to both methodology and application.