Simulation Intelligence and Learning for Autonomous Systems

Learning Goals

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.

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

C

Curriculum Industry
TAF Type

C

Curriculum Health
TAF Type

D

Curriculum Economy
TAF Type

D

SSD

INF/01 ING-INF/04

ECTS

6

Semester

1

Lecturers

Francesca Cairoli
Erica Salvato