COMPUTER SCIENCE II - MOD. 2

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%)

Frontal lectures, active learning, laboratory activity, seminars

Italian

written