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 and simulation of complex systems on different scales, to the identification of computational solutions, and the analysis of the simulated dynamics.

Knoweldge and understanding of the main methods for the modeling and simulation of complex systems. Understanding and evaluation of the problem's complexity. Ability of selecting developing the appropriate methods of simulation and analysis. Ability of implementing algorithmic solutions to possibly fill any gaps in the domain knowledge.

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
Agent-based simulation
Ordinary differential equations
Spatial simulation with PDEs
Hybrid multi-scale simulation
Phenomenological approaches
Complex dynamics analysis: parameter sweep, sensitivity analysis, parameter estimation, reverse engineering
Emergent phenomena in 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.

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

Frontal lectures, laboratory activity, seminars

Italian

written