This course offers a deep exploration of cutting-edge scientific fields, providing
students with a solid foundation and advanced insights.
The Simulation Intelligence module (3 CFU, 24h) will delve into the in-
triguing realm of Physics-Infused Surrogate Modeling where probabilistic and
differentiable programming are key. You will gain a thorough understanding of
how surrogate models can be effectively applied in simulation-based inference
techniques, enabling you to address complex problems. Additionally, we will
explore the critical aspects of causality and uncertainty, equipping you with the
necessary tools to navigate these challenges.
Transitioning to the Learning for Autonomous Systems module (3 CFU,
24h), we will embark on a comprehensive study of autonomous systems. We
will delve into optimal control problems, examining the underlying principles
and methodologies. Furthermore, we will explore the evolution from model-
based to model-free control strategies, providing you with a deep understanding
of their advantages and limitations. Through practical applications and real-
world examples, you will develop a holistic perspective on the capabilities and
challenges of autonomous systems.
Throughout the course, you will engage in a variety of activities designed to
enhance your learning experience. These include hands-on sessions where you
will apply theoretical concepts using practical tools and techniques. Our aim is
to ensure you have the knowledge and skills required to excel in these exciting
fields.
students with a solid foundation and advanced insights.
The Simulation Intelligence module (3 CFU, 24h) will delve into the in-
triguing realm of Physics-Infused Surrogate Modeling where probabilistic and
differentiable programming are key. You will gain a thorough understanding of
how surrogate models can be effectively applied in simulation-based inference
techniques, enabling you to address complex problems. Additionally, we will
explore the critical aspects of causality and uncertainty, equipping you with the
necessary tools to navigate these challenges.
Transitioning to the Learning for Autonomous Systems module (3 CFU,
24h), we will embark on a comprehensive study of autonomous systems. We
will delve into optimal control problems, examining the underlying principles
and methodologies. Furthermore, we will explore the evolution from model-
based to model-free control strategies, providing you with a deep understanding
of their advantages and limitations. Through practical applications and real-
world examples, you will develop a holistic perspective on the capabilities and
challenges of autonomous systems.
Throughout the course, you will engage in a variety of activities designed to
enhance your learning experience. These include hands-on sessions where you
will apply theoretical concepts using practical tools and techniques. Our aim is
to ensure you have the knowledge and skills required to excel in these exciting
fields.
- Teacher: ERICA SALVATO