Hours:
20 hours (5 credits)

Room:
Aula ADII3, Polo B Ingegneria, via G. Pisano

Introduction & motivations.:
Many complex systems developed by engineers (e.g. labs on chips, iPads, magnetic resonance imaging scanners, nationwide electrical/gas/oil transportation network, buildings/automotive/aircraft frames) or found in nature (e.g. the human cardiovascular system, the brain neural network, biological systems, the geophysical network of oil/water/gas reservoirs) can be viewed as large collections of interconnected dynamical system components. The performance and characteristics of each individual component, and hence the entire system, critically depend on what engineers or scientists refer to as "second-order effects". In addition, components are often affected by random uncertainties in material properties and geometries.

Target goal:
Provide students access to the state of the art in modeling, simulation, model order reduction and uncertainty quantification of a large variety of complex and multi-disciplinary dynamical systems, in order to help them with their diverse research projects involving modeling, analysis, design and optimization problems in a variety of different engineering and science disciplines dealing with complex systems: e.g. Electrical Engineering (interconnect networks including parasitics; full-wave electromagnetic structures; analog and digital circuits including nonlinear semiconductor devices and Micro­Electro­Mechanical Devices), Mechanical Engineering (frame modeling, heat diffusion, fluid-dynamics and oil transport), Civil Engineering (structural problems, vibrations), Material Sciences (inverse problems for identification of material properties), Biomedical Engineering (biochemical reactions and the human cardio­vascular system).

Class project & evaluation:
Students will be working in small teams on a course-long project involving modeling and simulation of a complex system either self-proposed from their own field of research, or chosen from a few examples developed in class. Time in class will include short lectures interleaved by numerous interactive and hands-on activities coordinated by the instructor and supporting the self-proposed projects. Final evaluations will be based on in-class work and interaction with the staff during the course and a final live demo presentation of their project. The focus of the course will not be on mathematical formalism and rigorous theorem proving, but rather on developing general intuition, creativity, practical implementation and model debugging skills.

Prerequisites:
Basic calculus, differential equations, linear algebra as well as some basic programming experience in Matlab, or in other programming languages for scientific computing.

Schedule:

• Day1 – June 5 – 9:30-12:30, 14:00-16:30
• Day2 – June 6 – 9:30-12:30, 14:00-16:30
• Day3 – June 7 – 9:30-13:30
• Day4 – June 8 – 10:30-12:30
• Day5 – June 9 – 10:30-12:30, 14:00-16:30