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
Rule-based modeling and network-free simulation
Spatial simulation with next subvolume method
Approximated Giillespie simulation
Langevin equations, SDEs, ODEs
Agent-based simulation, cellular automata, hybrid and multi-scale simulation
Complex dynamics analysis: parameter sweep, sensitivity analysis, parameter estimation, reverse engineering
Emergent phenomena in complex systems
Neural networks for the analysis of complex systems, physics-informed neural networks
Metodi avanzati di computational intelligence

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