Numerical Simulation Laboratory
A.Y. 2024/2025
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
The course aims to provide students with:
· advanced techniques for sampling random variables and simulate stochastic processes
· familiarity with the applications of these techniques to the simulation of complex systems
· an introduction to some computational intelligence techniques, machine learning and deep neural networks
· an introduction to parallel computation and parallel programming
· advanced techniques for sampling random variables and simulate stochastic processes
· familiarity with the applications of these techniques to the simulation of complex systems
· an introduction to some computational intelligence techniques, machine learning and deep neural networks
· an introduction to parallel computation and parallel programming
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
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
· Introduction to machine learning and deep neural networks
· 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
· Introduction to machine learning and deep neural networks
Prerequisites for admission
Knowledge of C ++ programming language
Teaching methods
Delivery method: traditional, lectures and numerical exercises
Attendance: mandatory
Attendance: mandatory
Teaching Resources
· E. Vitali, M. Motta, D.E. Galli "Theory and Simulation of Random Phenomena" Springer Unitext (in press)
· M.E.J. Newman and G.T. Barkema "Monte Carlo Methods in Statistical Physics", Clarendon Press
· D. Frenkel and B. Schmidt "Understanding Molecular Simulation", Academic Press
· W. Krauth "Statistical Mechanics -Algorithms and Computations" Oxford University Press
· P. Glasserman "Monte Carlo Methods in Financial Engineering" Springer
· The material presented and discussed in the individual lectures and laboratory exercises is made available on the Ariel website of the course.
· M.E.J. Newman and G.T. Barkema "Monte Carlo Methods in Statistical Physics", Clarendon Press
· D. Frenkel and B. Schmidt "Understanding Molecular Simulation", Academic Press
· W. Krauth "Statistical Mechanics -Algorithms and Computations" Oxford University Press
· P. Glasserman "Monte Carlo Methods in Financial Engineering" Springer
· The material presented and discussed in the individual lectures and laboratory exercises is made available on the Ariel website of the course.
Assessment methods and Criteria
The examination consists in the delivery of a series of numerical exercises and an oral discussion regarding the numerical exercises delivered in the light of the topics dealt with in the laboratory. The correctness of the numerical exercises, the quality of the data analysis carried out on the results of the simulations and the programming style are evaluated. At the end of the teaching the student will have to know advanced methods to sample random variables and know how to use them to set up simulations of complex systems.
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