Universitatea Babeş-Bolyai Cluj-Napoca
Facultatea de Matematică şi Informatică
Ciclul de studii: Masterat
Domeniul: Informatica
Programul de studii: Modelare si simulare - în limba engleză
Limba de predare: Engleză

SYLLABUS

I. General data
Code	Subject	
MID1034	Simulation Methods	

Semester	Hours: C+S+L+P	Category	Status	
2	2+1+0+1	speciality	compulsory	

II. Full status faculty members
Name and	Scientific	Didactic	Chair	Type of activity	
surname	title	title		C	S	L	
LIBAL Andras	Ph.D.	Lect.	Computer Systems	*			

Associated faculty members
Name and	Scientific	Institution	Type of	Type of activity	
surname	title		position	C	S	L	

III. Course objectives
The objective is to present to the students several important simulation methods used in scientific computing. Starting from molecular dynamics and Monte Carlo simulations, we proceed to show some of the general concepts and specific challenges in areas as different as materials science (micromagnetics) epidemiological modeling, climate science, computational neuroscience, computational fluid dynamics, traffic modeling and complex networks.	

IV. Course contents

V. Bibliography
1. Michael P. Allen: Introduction to Molecular Dynamics Simulation
2. Werner Krauth : Introduction To Monte Carlo Algorithms
3. Zoltán Néda : Monte Carlo methods for magnetic systems
4. Sam Northshield, Mike Daven, Alex Kheyfits, Melkamu Zeleke: An introduction to Cellular Automata and their applications (DIMACS Reconnect 2000)
5. http://math.nist.gov/oommf/

VI. Thematic of didactic activities per weeks
1. Introduction, Molecular Dynamics Simulation. Widely used MD packages (NAMD+VMD, LAMMPS),challenges and optimization in writing your own MD code (Verlet stepping methods, neighbor lookup list)
2. Monte Carlo Simulation, concept, problems suited for MC simulation
3. Cellular Automaton – what is a CA, Conway’s Game of Life
4. Micromagnetic simulation (OOMMF,nMAG), approximations made, understanding the Landau-Lifsitz-Gilbert equations, event-driven magnetics simulation 
5. Complex networks, scalefree networks, hubs, connection to graph theory, Albert-Barabási graphs 
6. Epidemological simulations, SI, SIR, SIS, SIRS, and other models
7. Computational Neuroscience – from the Hodgkin and Huxley model to models used today, challenges and applications
8. Protein Folding simulation, defining the problem and the importance of the subject, Levinthal’s paradox, simple examples (Fold@Home), and simple stick model
9. Finite Element Modeling: discretization, generating a mesh, stress-strain relationships
10. Computational Fluid Dynamics: Navier-Stokes equation and simplified versions
11. Climate Modeling
12. Traffic Modeling
13. Ray Tracing
14. Rubic’s cube: a systematic approach to a new problem	

VII. Didactic methods used
Presentation, discussions, writing their own simulation code, testing	

VIII. Assessment
60% on the activities during the year (tests, simulation code)
40% final exam (written, test style)
10% seminar activity bonus	

IX. Additional references

	Dean,							Chair head,

	Date,							Course responsible,
								Lect. LIBAL Andras, Ph.D.