Adaptive estimation of the 1D Pennes’ bio-heat equation using multiple-models | Andrea Cristofaro, Department of Computer, Control and Management Engineering, Sapienza University of Rome (Italy)

MC 6460 and Zoom https://uwaterloo.zoom.us/j/99020199127?pwd=ZnJRekRWdlV6MmVHRU9rQzR2NkNjZz09

Meeting ID: 990 2019 9127

Passcode: 763644

Speaker

Andrea Cristofaro, Department of Computer, Control and Management Engineering, Sapienza University of Rome (Italy)

Title

Adaptive estimation of the 1D Pennes’ bio-heat equation using multiple-models

Abstract

In this talk, I will present a multiple-model adaptive estimation setup for a class of uncertain parabolic reaction-diffusion PDEs encompassing the Pennes' bio-heat equation, which is a motivating case study from the perspective of biomedical applications such as hyperthermia. Allegedly, this could be one of the first times that multiple-models are applied to the adaptive estimation of partial differential equations.

The efficacy of the approach in estimating the system solution and recovering the value of the reaction coefficient is validated through numerical simulations in MATLAB. 

Bio

Andrea Cristofaro received the M.Sc. in Mathematics from Sapienza University of Rome (Italy) in 2005 and the PhD in Information Science and Complex Systems from University of Camerino (Italy) in 2010. After some years spent in France (INRIA Grenoble) and Norway (NTNU and University of Oslo) holding different academic positions, in 2019 he joined the Department of Computer, Control and Management Engineering, Sapienza University of Rome (Italy), where he is currently an Associate Professor. Andrea Cristofaro is author of more than 120 scientific papers, and he serves as Associate Editor for Automatica and IEEE Transactions on Control Systems Technology. He is also a member of the IEEE CSS Conference Editorial Board and of the Board of Experts of the European Science Foundation. He is recipient of a Marie-Curie fellowship, and his main research interests include: observers for linear and nonlinear systems, multi-agent and multi-robot systems, control of distributed parameter systems, hybrid systems.