Uncertainty Quantification in Computational Science and Engineering

Catalog description

This course is concerned with the modeling, identification, and propagation of model and parametric uncertainties in computational science and engineering. The aim is to provide decision makers, engineers and scientists with predictions endowed with measures of confidence. In practice, the randomness introduced within the modeling framework can reflect intrinsic stochasticity or some lack of knowledge. The covered material finds applications in a broad range of fields, from the modeling of materials and complex systems to robust design optimization. The course is oriented towards the understanding and implementation of state-of-the-art techniques for applied or fundamental research projects.