What to do when the size and complexity of your model essentially  prevent you from using it? Well, get a smaller and simpler model…

At the heart of this dimension reduction process is the notion of parameter importance which, ultimately, is part of the modeling process itself. Global Sensitivity Analysis (GSA) aims at efficiently  identifying important and non-important parameters; non-importance is important!  We will present in this talk advances and challenges in GSA; these will include how to deal with correlated variables, how to treat  time-dependent problems and stochastic problems and how to analyze the  robustness of GSA itself at low cost. The role played by surrogate models will also be discussed.

The discussion will be illustrated by an application from neurovascular modeling.

Joint work with Alen Alexanderian, Tim David, Joey Hart and Ralph Smith.