Simone Rusconi ha defendido su tesis doctoral

Simone Rusconi
  • La defensa tubo lugar en la Facultad de Ciencia y Tecnología de la Universidad del País Vasco, situada en el Campus de Leioa, el jueves 14 de junio 

Simone Rusconi se incorporó al Basque Center for Applied Mathematics com becario en el área de investigación de Modelización Matemática con Aplicaciones Disciplinarias en 2013 y posteriormente, se convirtió en estudiante de doctorado bajo la supervisión de Elena Akhmatskaya en 2014. Antes de llegar al BCAM, obtuvo una Licenciatura (2011) y un Máster (2014) en Ingeniería Matemática en el Politécnico de Milán.

Sus intereses de investigación incluyen modelos estocásticos y algoritmos de simulación Monte Carlo para varios procesos físicos, biológicos y químicos. Su tesis doctoral ha sido dirigida por la investigadora de Ikerbasque Elena Akhmatskaya, responsable del Grupo de Modelización y Simulación en Ciencias de la Vida y de los Materiales de BCAM, y por el investigador de Ikerbasque en la UPV/EHU Dmitri Sokolovski.

En nombre de todo el equipo de BCAM nuestra más sincera enhorabuena a Simone.

Title: Probabilistic Modelling of Classical and Quantum Systems

While probabilistic modelling has been widely used in the last decades, the quantitative prediction in stochastic modelling of real physical problems remains a great challenge and requires sophisticated mathematical models and advanced numerical algorithms. In this study, we developed the mathematical tools for solving three long-standing problems in Polymer Science and Quantum Measurement theory.

The question, “Why kinetic models cannot reproduce experimental observations in Controlled Radical Polymerization (CRP)?” has been answered by introducing in the kinetic model a delay and treating CRP as a non-Markovian process. The efficient stochastic simulation (SS) approach allowing for an accurate description of CRP has been formulated, theoretically grounded and tested using experimental data and the less advanced SS algorithms. An accurate prediction of a morphology development in multi-phase polymers is vital for synthesis of new materials but still not feasible due to its complexity. We proposed a Population Balance Equations (PBE)-based model and derived a conceptually new and computationally tractable numerical approach for its solution in order to provide a systematic tool for a morphology prediction in composite polymers.

Finally, we designed a stochastic simulation framework for continuous measurements performed on quantum systems of theoretical and experimental interest, which helped us to re-examine the “fuzzy continuous measurements” theory by Audretsch and Mensky (1997) and expose some of its deficiencies, while making amendments where necessary. All developed modelling approaches are general enough to be applied to the broad range of physical applications and thus ultimately to contribute to the understanding and prediction of complex chemical and physical processes.