KaterinaDr Katerina Konakli
ETH Zurich, Chair of Risk, Safety & Uncertainty Quantification
Low-rank tensor approximations versus polynomial chaos expansions for uncertainty propagation and reliability analysis
Thursday, October 8, 2015, 2:30pm to 3:30pm | Room 5-314

Modern engineering faces the challenge of uncertainty propagation through increasingly complex computational models. A remedy is to substitute expensive-to-evaluate models with so-called meta-models, which possess similar statistical properties, while maintaining simple functional forms. Polynomial chaos expansions have proven an effi- cient meta-modeling technique in a wide range of applications, but suf- fer from the curse of dimensionality. A promising alternative for build- ing meta-models with polynomial bases in high-dimensional spaces is the newly emerged technique of low-rank tensor approximations. In this talk, open questions in the construction of such approximations will first be discussed. In the sequel, the newly emerged approach will be confronted with polynomial chaos expansions in applications involving models of different dimensionality. Special emphasis will be given on the estimation of the response distribution at the tails, which is critical for evaluating rare-event probabilities in reliability analysis.

Short Bio

Katerina Konakli is a post-doctoral researcher in the group of Risk, Safety and Uncertainty Quantification at the Swiss Federal Institute of Technology (ETH) in Zurich. Originally from Greece, Katerina conducted her graduate studies at the University of California, Berkeley, where she earned a Master's and a PhD degree in Civil and Environmental Engineering and a Master's degree in Statistics. Prior to her current appointment, she served as a post-doctoral researcher in the Civil Engineering Department at the Technical University of Denmark. Katerina's research has spanned the areas of stochastic dynamics, decision analysis and uncertainty quantification using surrogate models.