Kaiserslautern  /  September 26, 2018  -  September 28, 2018

Uncertainty Quantification

International Autumn Workshop of the Felix Klein Academy

Complex processes in nature and technology often do not proceed as expected. Can their behavior still be predicted? Mathematicians are currently working on finding quantitative descriptions for situations with many unknown parameters.

Multilevel Monte Carlo Methods, Markov Chain Monte Carlo Methods, as well as tensor decomposition and Deep Learning methods for multivariate function approximation, are among the most advanced and efficient methods for Uncertainty Quantification.

Three international experts from the United States, Great Britain and Russia will provide survey talks and current research topics with particular emphasis on how to cope with uncertainty. The objective of the International Autumn Workshop of the Felix Klein Academy is to stimulate research and to discuss applications.

 

These experts present their recent research work:

  • Prof. Yalchin Efendiev, PhD: Texas A&M University (TAMU), College Station, USA
  • Prof. Robert Scheichl, PhD: University of Bath, Great Britain
  • Prof. Ivan Oseledets, PhD: Skolkovo Institute of Science and Technology, Moscow, Russia