Uncertainty Quantification (UQ)

What is UQ?

Uncertainty Quantification (UQ) is an essential tool that connects the three pillars of science – theory, experimentation, and computation – and allows us to better understand and manage uncertainties in the scientific discovery and decision-making process. By leveraging the latest UQ techniques and methods, we can more effectively guide our research and make more informed decisions.

The use of computational models in scientific discovery, engineering design, and decision support is often hampered by errors and uncertainties. These inherent uncertainties can arise from a variety of sources, including experimental errors, inadequacies in the model structure, uncertainties in model parameters and initial conditions, and errors due to sampling and numerical discretization. To overcome these challenges and improve the accuracy and reliability of computational models, it is essential to develop and apply advanced AI & UQ techniques and methods.

As a graduate student in our UQ lab, you will have the opportunity to work on a range of exciting research projects that span the development of new methodologies and algorithms, as well as the application of these methods in collaboration with researchers in various scientific and engineering disciplines. This will give you a unique perspective on the entire research process, from methodology development to application, and will provide you with a well-rounded education in AI & UQ. You will gain valuable skills in collaboration, communication, and critical thinking, and will have the opportunity to make a real impact in the field of AI & UQ research.