ECTS - Mathematical Modeling via Differential and Difference Equations
Mathematical Modeling via Differential and Difference Equations (MDES610) Course Detail
Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
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Mathematical Modeling via Differential and Difference Equations | MDES610 | Area Elective | 3 | 0 | 0 | 3 | 5 |
Pre-requisite Course(s) |
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Math 276 Differential Equations or Math 262 Ordinary Differential Equations |
Course Language | English |
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Course Type | Elective Courses |
Course Level | Natural & Applied Sciences Master's Degree |
Mode of Delivery | Face To Face |
Learning and Teaching Strategies | Lecture. |
Course Lecturer(s) |
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Course Objectives | Differential and difference equations constitute main tools that scientists and engineers use to make mathematical models of important practical problems. This course aims to involve engineering students in mathematical modelling by means of differential and difference equations and to develop skill with solution techniques in order to understand complex physical phenomena. |
Course Learning Outcomes |
The students who succeeded in this course;
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Course Content | Differential equations and solutions, models of vertical motion, single-species population models, multiple-species population models, mechanical oscillators, modeling electric circuits, diffusion models, modeling by means of difference equations. |
Weekly Subjects and Releated Preparation Studies
Week | Subjects | Preparation |
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1 | Some terminology. Examples. Separation of variables. | Read related sections in references |
2 | The Euler method. Linear differential equations with constant coefficients. | Read related sections in references |
3 | Vertical motion without air resistance. Vertical motion with air resistance. | Read related sections in references |
4 | Simple population model. Population with emigration. | Read related sections in references |
5 | Population with competition (the logistic equation). | Read related sections in references |
6 | Predator-prey (fox-rabbit) population model. Epidemics (SIR). Two-species competition. | Read related sections in references |
7 | Spring-mass without damping or forcing. Spring-mass with damping and forcing. | Read related sections in references |
8 | Pendulum without damping. Approximate pendulum without damping. | Read related sections in references |
9 | Series RC charge. Series RLC charge and current (first-order system). | Read related sections in references |
10 | Parallel RLC voltage (second-order scalar equation). | Read related sections in references |
11 | Diffusion without convection or source. Diffusion with convection and source. | Read related sections in references |
12 | Heat flow without heat source. Time-dependent diffusion. | Read related sections in references |
13 | Basics of difference equations | Read related sections in references |
14 | A crystal lattice. | Read related sections in references |
15 | Overall review | - |
16 | Final exam | - |
Sources
Course Book | 1. P. W. Davis, Differential Equations: Modeling with matlab, Prentice Hall, Upper Saddle River, New Jersey, 1999. |
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2. W. G. Kelley and A. C. Peterson, Difference Equations: An Introduction with Applications, Academic Press, New York, 1991. | |
Other Sources | 3. E. Kreyszig, Advanced Engineering Mathematics, 8th ed., Wiley, New York, 1999. |
4. S. L. Ross, Differential Equations, 3rd ed.,Wiley, New York, 1984. | |
5. S. Elaydi, An Introduction to Difference Equations, Springer-Verlag, New York, 1996. |
Evaluation System
Requirements | Number | Percentage of Grade |
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Attendance/Participation | - | - |
Laboratory | - | - |
Application | - | - |
Field Work | - | - |
Special Course Internship | - | - |
Quizzes/Studio Critics | - | - |
Homework Assignments | 5 | 30 |
Presentation | - | - |
Project | - | - |
Report | - | - |
Seminar | - | - |
Midterms Exams/Midterms Jury | 2 | 35 |
Final Exam/Final Jury | 1 | 35 |
Toplam | 8 | 100 |
Percentage of Semester Work | 65 |
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Percentage of Final Work | 35 |
Total | 100 |
Course Category
Core Courses | X |
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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 | ||||
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1 | 2 | 3 | 4 | 5 | ||
1 | Has the ability to apply scientific knowledge gained in the undergraduate education and to expand and extend knowledge in the same or in a different area. | |||||
2 | Has the ability to obtain, to evaluate, to interpret and to apply information by doing scientific research. | |||||
3 | Can apply gained knowledge and problem solving abilities in inter-disciplinary research. | |||||
4 | Has the ability to work independently within research area, to state the problem, to develop solution techniques, to solve the problem, to evaluate the obtained results and to apply them when necessary. | |||||
5 | Takes responsibility individually and as a team member to improve systematic approaches to produce solutions in unexpected complicated situations related to the area of study. | |||||
6 | Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework. | |||||
7 | Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility. | |||||
8 | Has the ability to follow recent developments within the area of research, to support research with scientific arguments and data, to communicate the information on the area of expertise in a systematically by means of written report and oral/visual presentation. | |||||
9 | Has proficiency in English language and has the ability to communicate with colleagues and to follow the innovations in mathematics and related fields. | |||||
10 | Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge. | |||||
11 | Has professional 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. |
ECTS/Workload Table
Activities | Number | Duration (Hours) | Total Workload |
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Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
Laboratory | |||
Application | |||
Special Course Internship | |||
Field Work | |||
Study Hours Out of Class | 16 | 2 | 32 |
Presentation/Seminar Prepration | |||
Project | |||
Report | |||
Homework Assignments | 5 | 6 | 30 |
Quizzes/Studio Critics | |||
Prepration of Midterm Exams/Midterm Jury | 2 | 8 | 16 |
Prepration of Final Exams/Final Jury | 1 | 10 | 10 |
Total Workload | 136 |