| Dimitris Kamilis, University of Edinburgh Institute for Digital Communications (IDCOM) |
| Tue 29 Mar 2016, 13:00 - 14:00 |
| AGB Seminar Room AGB Building, King’s Buildings, EH9 3JL |
If you have a question about this talk, please contact: Iman Tavakkolnia (s1371647)
Abstract: In the Bayesian approach to controlled-source electromagnetic imaging, the objective is to infer the conductivity random field based on finite, noisy measurements of the electromagnetic field. When the unknown field is represented in some basis, we talk about distributed, parametric uncertainty. We present a methodology to solve the pertinent non-linear, high-dimensional Bayesian inverse problem by addressing the numerical solution of the parametric, deterministic Maxwell's equations in combination with adaptive sparse-grid quadrature and model order reduction.
Biography: Dimitris Kamilis holds a Physics degree from the Aristotle University of Thessaloniki,Greece. He has received an M.Sc. in Theoretical Physics from Imperial College London and an M.Sc. in Computational Science & Engineering from Umeå university, Sweden. Since 2013 he has been working toward the PhD degree in the Agile Tomography group, IDCoM, University of Edinburgh. His research is focused on classical and Bayesian electromagnetic inverse problems with emphasis in high-dimensional models.