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 Lecturer(s) |
|
| 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;
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| 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 | |
|---|---|
| Major Area Courses | X |
| 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 | Acquires skills to use the advanced theoretical and applied knowledge obtained at the mathematics bachelors program to do further academic and scientific research in both mathematics-based graduate programs and public or private sectors. | X | ||||
| 2 | Transplants and applies the theoretical and applicable knowledge gained in their field to the secondary education by using suitable tools and devices. | X | ||||
| 3 | Acquires the skill of choosing, using and improving problem solving techniques which are needed for modeling and solving current problems in mathematics or related fields by using the obtained knowledge and skills. | X | ||||
| 4 | Acquires analytical thinking and uses time effectively in the process of deduction. | X | ||||
| 5 | Acquires basic software knowledge necessary to work in the computer science related fields and together with the skills to use information technologies effectively. | X | ||||
| 6 | Obtains the ability to collect data, to analyze, interpret and use statistical methods necessary in decision making processes. | X | ||||
| 7 | Acquires the level of knowledge to be able to work in the mathematics and related fields and keeps professional knowledge and skills up-to-date with awareness in the importance of lifelong learning. | X | ||||
| 8 | Takes responsibility in mathematics related areas and has the ability to work affectively either individually or as a member of a team. | X | ||||
| 9 | Has proficiency in English language and has the ability to communicate with colleagues and to follow the innovations in mathematics and related fields. | X | ||||
| 10 | Has the ability to communicate ideas with peers supported by qualitative and quantitative data. | X | ||||
| 11 | Has professional and ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications. | 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 | ||