GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
NUMERICAL SOLUTIONS OF PARTIAL DIFFERENTIAL EQUATIONS BY FINITE DIFFERENCE METHOD/6341305
Course Title: NUMERICAL SOLUTIONS OF PARTIAL DIFFERENTIAL EQUATIONS BY FINITE DIFFERENCE METHOD
Credits 0 ECTS 7.5
Course Semester 1 Type of The Course Elective
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
Learning how to solve initial and boundary value problem approximately even tehere no analytical solutions.
The importance of using computers in many fields such as mathematics, engineering and etc. are also shown effectively.
Content of the lecture is associated with the other areas of mathematics such as algebra, analysis etc. Students have gained
knowledge about computer programming and preparing algorithms.
Put emphasis on the relations between differential equations and mathematical models.
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week  parabolic equations: Alternative Derivation of difference Equations
2. Week  Reduction to system of ordinary equations
3. Week  Pade Aproximation to Exp Function
4. Week  Standard finite-difference equations via the Pade approximation
5. Week  Crank-Nicholson method
6. Week  Sybmol for the extrapolation method
7. Week  The Local truncation errors and symbols of extrapolation schemas
8. Week   Eigenvalue- Eigenvector solutions of a system of ordinary differential equations
9. Week  Eigenvalue- Eigenvector solutions of a system of ordinary differential equations
10. Week  Use of three time level difference equations
11. Week  The analytical solution of the classical explicit approximation to the heat equation
12. Week  Hyperbolic equations and Characteristic
13. Week  Finite-Difference methods on Rectangular mesh for first-order equations
14. Week   Finite Difference Formulaes in Polar Coordinates
15. Week  
16. Week  
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
30
 Assignment
1
10
 Application
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
40
 Percentage of Final Exam to Total Score  
60
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
0
 Reading Tasks
4
12
48
 Searching in Internet and Library
4
12
48
 Material Design and Implementation
0
 Report Preparing
0
 Preparing a Presentation
0
 Presentation
1
15
15
 Midterm Exam and Preperation for Midterm Exam
1
15
15
 Final Exam and Preperation for Final Exam
1
5
5
 Other (should be emphasized)
1
15
15
 TOTAL WORKLOAD: 
188
 TOTAL WORKLOAD / 25: 
7.52
 Course Credit (ECTS): 
7.5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1To be able to develop their knowledge of theory and applications at master degree depending on the competencies acquired in undergraduate level.X
2To be able to recognize different problems encountered in mathematics and to be able to make studies for their solutions.X
3To be able to formulate new solutions with scientific methods mostly based on analysis and synthesis.X
4To be able to carry out his/her works either independently or in groups within a project considering his/her knowledge on the field he/she works in a critical approach.X
5To be able to continue his/her works considering social, scientific and ethical values.X
6To be able to follow scientific and social developments related to his/her field.X
7To be able to carry out his/her works within the framework of quality management, workplace safety and environmental awareness and to be able to present systematically his/her works using various methods like written, oral or visual.X
8To be able to use methods of accessing knowledge effectively in accordance with ethical values.X
9To be able to use knowledge in other disciplines by combining it with mathematical information.X
10To be able to make activities in the awareness of need for lifelong learning.X
11To be able to make connections between mathematical and social concepts and produce solutions with scientific methods.X
12To be able to use his/her mathematical knowledge in technology.X
 -- NAME OF LECTURER(S)
   ( Prof. Dr. Fatma AYAZ)
 -- WEB SITE(S) OF LECTURER(S)
   ()
 -- EMAIL(S) OF LECTURER(S)
   (fayaz@gazi.edu.tr )