The course will provide the student knowledge in the area of simulation, control and design of digital twin (DT).
The first part presents techniques for the integration of machine learning with computer simulation, such as surrogate modelling, simulation-based inference and continuous data integration.
The second part focuses on data-driven model-free control approaches whose synthesis relies on the availability of accurate simulators.
Students will learn how to properly use these tools to effectively model, control and analyse complex systems.
Simulation Intelligence and Learning for Autonomous Systems
Learning Goals
Program in pills
Surrogate modelling, simulation-based inference, causal modelling and inference, differentiable programming, co-simulation, uncertainty quantification, basics of dynamical systems, dynamic programming, optimal and learning-based control.
Area
Machine Learning and Artificial Intelligence
Curriculum Foundations
TAF Type
Curriculum Industry
TAF Type
Curriculum Health
TAF Type
Curriculum Economy
TAF Type
SSD
ECTS
Semester
Lecturers
Francesca Cairoli
Erica Salvato