9th Jan 2015

Current computational models are capable of generating petabyte-sized databases providing insight into processes taking place at extremely fine spatio-temporal scales. Apart from the obvious difficulties in visualizing, storing and processing such data, the fundamental challenge lies in extracting knowledge about the salient features and understanding of the driving mechanisms.

This is more pronounced in the context of multiscale simulations which can only be enabled by the ability to identify accurate but computationally-tractable reduced descriptions even in cases where physical insight is unavailable. Learning from data and identifying structural characteristics is a typical task in several computational statistics and machine learning applications.