Stefano Riva
PhD thesis title: Development of data-driven Reduced Order Modelling with application to MultiPhysics system
Academic Tutor: Francesca Giacobbo
Academic Supervisor: Antonio Cammi and Carolina Introini
PhD cycle: 37° (see all student profiles of the same cycle > LINK)
BSc: Energy Engineering, Politecnico di Milano
MSc: Nuclear Engineering, Politecnico di Milano
``Giovani Laureati`` by cultural association CISE2007 / ``Best Paper`` at ICAPP2023 conference
Thesis abstract
My research project aims to develop data-driven Reduced Order Modelling techniques for nuclear reactors. The goal of this study is to implement efficient tools that can conduct fast and precise simulations for multi-query or control scenarios by combining information from numerical simulations and experimental data. Specifically, I will investigate the feasibility of updating and/or correcting predictions made by a multiphysics model using real-world evaluations of the system. Moreover, I will assess the reliability of indirect reconstruction algorithms in estimating a quantity of interest based on measurements of another variable.
Personal interest in my research theme
The field of Reduced Order Modelling holds a lot of potentials and can serve as a crucial tool for the design and control of nuclear reactors. Leveraging mathematical models, it enables the reduction of computational costs in numerical solutions. The strong connection between the engineering and mathematical realms is a key factor that led me to dedicate myself to this research area. Throughout my academic journey, I have consistently been driven by curiosity, particularly in exploring the interplay between these domains.
