COS 504: Simulations for Physical Systems
Course Title |
Simulations for Physical Systems |
Course Code |
COS 504 |
Course Type |
Elective |
Level |
PhD |
Instructor’s Name |
Assoc. Prof. Giannis Koutsou (Lead Instructor), Dr. Jacob Finkenrath, Dr. Simone Bachhio Guest Lecturer: Prof. Constantia Alexandrou
|
ECTS |
5 |
Lectures / week |
2 (90 min. each) 4.5 weeks |
Laboratories / week |
2 (90 min. each) 2.5 weeks |
Course Purpose and Objectives |
The course aims at teaching students to apply high-performance computing and data analysis approaches to solve complex physical systems. Students will learn to handle a range of applications from condensed matter and biophysics to particle and nuclear physics. |
Learning Outcomes |
Students will: - learn to describe and analyze non-linear systems and systems with many degrees of freedoms
- develop algorithms, optimize and implement them on large computers
- learn state-of-the-art simulations approaches such as Markov Chain Monte Carlo
- study phase transitions and critical behavior using simulations and deep learning approaches
- implement crowd simulation such as particle and agent based models for a range of self-organized dynamics of structures
- use a range of data analysis methods such as jackknife and bootstrap resampling, Bayesian statistical analysis,
- aquire a set of the High Performance Computing and data analysis skills and employ them for solving physical systems. These skills are applicable to a range of problems in chemistry, biology and engineering.
|
Prerequisites |
None |
Background Requirements |
Knowledge of a low-level programming languages such as Fortran, C, C++ and parallel programing including MPI |
Course Content |
Week 1-2
Numerical solution of partial differential equations, such as the wave, diffusion and Schrödinger’s equations
Week 3 Introduction to minimization algorithms
Week 4 Data analysis of correlated data sets, resampling and Bayesian approaches
Week 5-6 Phase transitions in physical systems, critical behaviour, identification using deep learning methods
Week 7 Markov processes and Monte Carlo methods for many body systems
|
Teaching Methodology |
- 9 x 1.5 h lectures |
Bibliography |
- Course notes
- Monte Carlo Methods, Malvin H. Kalos and Paula A. Whitlock
|
Assessment |
The following assessment methods will be combined for the final grade:
- Coursework
- A final project |
Language |
English |