Title of the course: An Introduction to Scientific Machine Learning
Lecturer: Paola Gervasio
Objective of the course: Understand the foundational concepts of machine learning and how they apply to problems in science and engineering. Describe key methodologies used in SciML, including data-driven modeling, physics-informed neural networks (PINNs), and hybrid modeling approaches.
- Teacher: Paola GERVASIO
- Teacher: Lorenzo GHIRO
- Teacher: RENATO ANTONIO LO CIGNO
- Teacher: Mariasole BANNÒ
- Teacher: Elisabetta CERETTI
- Teacher: Laura Eleonora DEPERO
- Teacher: Stefania FEDERICI
- Teacher: Antonio FIORENTINO
- Teacher: Marcello Giuseppe GELFI
- Teacher: MASSIMILIANO GRANIERI
- Teacher: Davide PICCHI
- Teacher: Alberto SALVADORI
- Teacher: Marialuisa VOLTA
This is a PhD level course on stochastic modeling. The course touches on many topics and arguments that spin aroung the Art of Modeling and reasons to use stachastic models and not deterministic ones, which normally can capture only the average behavior of a system.
- Teacher: RENATO ANTONIO LO CIGNO
- Teacher: ILEANA ARMANDO
- Teacher: Nicola Francesco LOPOMO
- Teacher: chiara pasini
- Teacher: gianluca rossetto
- Teacher: Mauro SERPELLONI
- Teacher: giampiero pasquale sorrentino
- Teacher: Claudio CARNEVALE
- Teacher: Laura Eleonora DEPERO
- Teacher: SANAE DOURHNOU
- Teacher: Stefania FEDERICI
- Teacher: Pietro POESIO
- Teacher: Paolo SECCHI