COMPUTATIONAL INTELLIGENCE

The course is one of the fundamental courses of the Bachelor's Degree in Physical Engineering and provides an introduction to the modeling, simulation and analysis of complex systems on different scales, to the identification of computational solutions, and the analysis of the simulated dynamics.

Knowledge and understanding of the main methods for the modeling and simulation of complex systems.
Knowledge and understanding of Computational Intelligence methods to tackle uncertainty and perform the inference of missing data.
Understanding and evaluation of the problem's complexity.
Ability to select and develop the appropriate methods of simulation and analysis.

Knowledge of Python programming. Basic knoweldge of probability and statistics.

Modeling and simulation of complex systems
Random numbers generation
Markov processes and Gillespie's Stochastic Simulation Algorithm
Advanced modeling and simulation methods: approximate (tau leaping), rule-based, network-free, spatial simulation
Agent-based simulation
Cellular automata
Integration of ordinary differential equations, hybrid and multi-scale simulation
Langevin equations and stochastic differential equations
Complex dynamics analysis: parameter sweep, sensitivity analysis
Parameter estimation and reverse engineering with evolutionary computation and swarm intelligence
Multi-objective optimization
Emergent phenomena in complex systems: multistability, robustness, chaos
Neural networks for the analysis of complex systems

Teaching material made available by the teacher.

Optional readings:
Munsky, Brian, William S. Hlavacek, and Lev S. Tsimring, eds. "Quantitative biology: theory, computational methods, and models". MIT Press, 2018.
Vanneschi, Silva. "Lectures On Intelligent Systems". Springer, 2023.

Written exam (70%) and project work (30%)

written

Regarding the grading, regardless of whether the student is attending or non-attending:

Scores in the 18–22 range will be assigned when there is:

- sufficient knowledge and applied understanding of the course content;
- limited ability to interpret results;
- sufficient communication skills, especially in terms of command of language and technical concepts.

Scores in the 23–26 range will be assigned when there is:
- fair knowledge and applied understanding of the course content;
- fair ability to interpret results;
- fair communication skills, especially in terms of command of language and technical concepts.

Finally, scores in the 27–30 range will be assigned when there is:
- good or excellent knowledge and applied understanding of the course content;
- good or excellent ability to interpret results;
- good or excellent communication skills, especially in terms of command of language and technical concepts.

30 "cum laude" will be awarded in cases of excellence across all these aspects.

Frontal lectures, active learning, laboratory activity, seminars