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COS 504: Simulations for Physical Systems


Course Title

Simulations for Physical Systems

Course Code

COS 504

Course Type




Instructor’s Name

Assoc. Prof. Giannis Koutsou (Lead Instructor), Dr. Jacob Finkenrath, Dr. Simone Bachhio

Guest Lecturer: Prof. Constantia Alexandrou



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.



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
-  5 x 1.5 h hands-on sessions
-  3 homework assignments
-  Presentation of final project


-  Course notes
-  Monte Carlo Methods, Malvin H. Kalos and Paula A. Whitlock


The following assessment methods will be combined for the final grade:
-  Coursework
-  A final project



Publications & Media