Oak Ridge, TN
September 20, 2018
Abstract: 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.
Biography: Dr. Gremaud received his PhD from Ecole Polytechnique Federale de Lausanne, Switzerland in 1991. He is a full-time professor at North Carolina State University, where he serves as Director of Graduate Programs. He is also Deputy Director, Statistical and Applied Mathematical Sciences Institute.