- Pierre Gremaud, North Carolina State University, Raleigh
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 the 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 unimportant parameters. The speaker will present advances and challenges in GSA, such as
-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 speaker will also discuss the role played by surrogate models. The discussion will be illustrated by an application from neurovascular modeling.
About the Speaker:
Dr. Gremaud is a professor at North Carolina State University, where he serves as Director of Graduate Programs. He is also Deputy Director of the Statistical and Applied Mathematical Sciences Institute.