Numerical Simulation Laboratory
A.Y. 2018/2019
Learning objectives
Simulation is an essential tool in studying complex systems, anticipating, complementing and reinforcing both experimental and theoretical approaches. The purpose of this computing laboratory is to introduce and apply advanced Monte Carlo sampling and other techniques to perform simulations of complex systems and to solve complex numerical tasks.
The course aims to provide students with:
1) advanced techniques for sampling random variables and simulate stochastic processes
2) familiarity with the applications of these techniques to the simulation of complex systems
3) an introduction to some computational intelligence techniques
4) an introduction to parallel computation and parallel programming
The course aims to provide students with:
1) advanced techniques for sampling random variables and simulate stochastic processes
2) familiarity with the applications of these techniques to the simulation of complex systems
3) an introduction to some computational intelligence techniques
4) an introduction to parallel computation and parallel programming
Expected learning outcomes
Undefined
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
· Probability theory, stochastic processes, mathematical statistics
· Sampling of random variables and Monte Carlo integration
· Markov chains, Metropolis algorithm
· Numerical simulations in classical and quantum statistical mechanics
· Stochastic calculus and stochastic differential equation with applications
· Computational intelligence, stochastic optimization, statistical analysis of inverse problems
· Introduction to parallel computing and parallel programming
· Sampling of random variables and Monte Carlo integration
· Markov chains, Metropolis algorithm
· Numerical simulations in classical and quantum statistical mechanics
· Stochastic calculus and stochastic differential equation with applications
· Computational intelligence, stochastic optimization, statistical analysis of inverse problems
· Introduction to parallel computing and parallel programming
FIS/01 - EXPERIMENTAL PHYSICS
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS
FIS/03 - PHYSICS OF MATTER
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS
FIS/05 - ASTRONOMY AND ASTROPHYSICS
FIS/06 - PHYSICS OF THE EARTH AND OF THE CIRCUMTERRESTRIAL MEDIUM
FIS/07 - APPLIED PHYSICS
FIS/08 - PHYSICS TEACHING AND HISTORY OF PHYSICS
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS
FIS/03 - PHYSICS OF MATTER
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS
FIS/05 - ASTRONOMY AND ASTROPHYSICS
FIS/06 - PHYSICS OF THE EARTH AND OF THE CIRCUMTERRESTRIAL MEDIUM
FIS/07 - APPLIED PHYSICS
FIS/08 - PHYSICS TEACHING AND HISTORY OF PHYSICS
Laboratories: 36 hours
Lessons: 24 hours
Lessons: 24 hours
Professor:
Galli Davide Emilio
Professor(s)
Reception:
Wednesday 14:30-16:00, or in other days by appointment (contact me by e-mail or telephone)
Dip. di Fisica, stanza A/T/S5b (piano 0 edificio LITA), via Celoria, 16