ECTS - Partial Differential Equations

Partial Differential Equations (MATH378) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Partial Differential Equations MATH378 Area Elective 3 0 0 3 6
Pre-requisite Course(s)
(MATH262 veya MATH276)
Course Language English
Course Type Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Discussion, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives Partial differential equations (PDEs) arise in connection with various physical and geometrical problems when the functions involved depend on two or more independent variables, usually on time t and on one or several space variables. In this course some of the most important PDEs occuring in physical and engineering applications are considered. Methods for solving initial and boundary value problems for PDEs are developed.
Course Learning Outcomes The students who succeeded in this course;
  • At the end of the course the students are expected to: 1- know Partial Differential Equations (PDEs) that arise in connection with various physical and geometrical problems 2- learn linear and non-linear, first order and higher order PDEs . 3- understand and determine how and when a PDE has a solution and which method is suitable to solve such a PDE. 4- learn methods such as separation of variables for solving initial and boundary value problems for PDEs .
Course Content Basic concepts; first-order partial differential equations; types and normal forms of second-order linear partial differential equations; separation of variables; Fourier series; hyperbolic, parabolic and elliptic equations; solution of the Wave Equation.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 1. Week The concept of partial differential equation (PDE). Linearity. Superposition principle. Linear and quasilinear first order equations. Method of Lagrange. 2. Week Linear and quasilinear first order equations. Method of Lagrange. 3. Week Linear and quasilinear first order equations. Method of Lagrange. 4. Week Classification of the Second Order Linear Partial Differential Equations, Reducing the hyperbolic, parabolic, and elliptic equations to canonical form and solving the resulting equations. 5. Week Classification of the Second Order Linear Partial Differential Equations, Reducing the hyperbolic, parabolic, and elliptic equations to canonical form and solving the resulting equations. 6. Week Classification of the Second Order Linear Partial Differential Equations, Reducing the hyperbolic, parabolic, and elliptic equations to canonical form and solving the resulting equations. 7. Week Separated solution. Separated solutions of Heat, Wave and Laplace’s equations with boundary conditions. 8. Week Midterm Exam 9. Week Separated solution. Separated solutions of Heat, Wave and Laplace’s equations with boundary conditions. 10. Week Separated solution. Separated solutions of Heat, Wave and Laplace’s equations with boundary conditions. 11. Week Fourier Series, Periodic Functions, Trigonometric Series. 12. Week Functions of any period, Even and Odd Functions. Half-range expansions. 13. Week The one and two-dimensional wave equations, Phsical interpretation of the solution of the wave equation. 14. Week Solution of the one and two-dimensional wave equations by means of D’Alembert’s solution with initial conditions. 15. Week Review of the course. 16. Week Final Exam

Sources

Course Book 1. [1] Elements of Partial Differential Equations, Ian N. Sneddon, First Edition, Dover Publications, Mineola, New York, 2006.
Other Sources 2. [2] Tyn Myint-U and Lokenath Debnath, Linear Partial Differential Equations for Scientists and Engineers, Fourth Edition, Birkhaeuser, Boston, 2007. [3] Rene Dennemeyer, Introduction to Partial Differential Equations and Boundary Value Problems, Thirte
3. [4] Erwin, Kreyszig, Advanced Engineering Mathematics, 8th Edition, John Willy & Sons, 1999.2.Numerical Solution of Partial Differential Equations: Finite Difference Methods by G.D. Smith, Clarendon Press, Oxford, 1985.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 60
Final Exam/Final Jury 1 40
Toplam 3 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Acquiring the skills of understanding, explaining, and using the fundamental concepts and methods of economics
2 Acquiring the skills of macro level economic analysis
3 Acquiring the skills of micro level economic analysis
4 Understanding the formulation and implementation of economic policies at the local, national, regional, and/or global level
5 Learning different approaches on economic and related issues
6 Acquiring the quantitative and/or qualitative techniques in economic analysis X
7 Improving the ability to use the modern software, hardware and/or technological devices
8 Developing intra-disciplinary and inter-disciplinary team work skills X
9 Acquiring an open-minded behavior through encouraging critical analysis, discussions, and/or life-long learning
10 Adopting work ethic and social responsibility
11 Developing the skills of communication.
12 Improving the ability to effectively implement the knowledge and skills in at least one of the following areas: economic policy, public policy, international economic relations, industrial relations, monetary and financial affairs. X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 14 4 56
Presentation/Seminar Prepration
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 18 36
Prepration of Final Exams/Final Jury 1 24 24
Total Workload 116