Course Identification

Dynamical Systems
20194132

Lecturers and Teaching Assistants

Prof. Vered Rom-Kedar
Ori Katz, Dr. Michal Pnueli

Course Schedule and Location

2019
Second Semester
Wednesday, 15:15 - 17:00, Ziskind, Rm 155

Tutorials
Monday, 13:15 - 14:00, Ziskind, Rm 155
27/03/2019

Field of Study, Course Type and Credit Points

Mathematics and Computer Science: Lecture; Elective; Regular; 3.00 points
Physical Sciences: Lecture; Elective; 3.00 points
Chemical Sciences: Lecture; Elective; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; Elective; 3.00 points

On the following four Mondays there will be a lecture on Mondays (instead of a tutorial), 13:00-14:30 :

25/3 (first class)
6/5
3/6
17/6

On the other Mondays there will be the regular tutorial 13:15-14:00

The last lecture will be on 3/7, last tutorial on 8/7

Prerequisites

To participate students should have good mathematical background in linear algebra, differential equations and some functional analysis.

30

English

Attendance and participation

Expected and Recommended

Numerical (out of 100)

50%
10%
40%

Take-home exam

22/07/2019
N/A
-
N/A

4

Syllabus

The course will introduce the students to some basic mathematical concepts of dynamical system theory and chaos. These concepts will be demonstrated using simple fundamental model systems based on discrete maps and ordinary differential equations. The solution structure will be studied by  both analytic and numerical methodologies. Motivation for the models arising in various fields of physics and biology will be discussed. The aim of this course is to provide the students with concrete approaches and geometrical intuition to modeling so as to provide them with working ability with non-linear systems.

Learning Outcomes

Upon successful completion of this course students should be able to:

1. Qualitatively classify the type of behaviors that may arise in some classes of differential equations and maps.
2. Demonstrate knowledge of mathematical modeling in several areas of research and to numerical simulations of such models.
3. Critically evaluate the outcome of such modeling approaches and simulations.