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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of COMPUTER ENGINEERING
Computer Engineering, Masters with Thesis
Course Catalog
http://ceng.ktu.edu.tr
Phone: +90 0462 3773157
FBE
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of COMPUTER ENGINEERING / Computer Engineering, Masters with Thesis
Katalog Ana Sayfa
  Katalog Ana Sayfa  KTÜ Ana Sayfa   Katalog Ana Sayfa
 
 

BILL5220Scientific Computing3+0+0ECTS:7.5
Year / SemesterFall Semester
Level of CourseSecond Cycle
Status Elective
DepartmentDEPARTMENT of COMPUTER ENGINEERING
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 3 hours of lectures per week
LecturerProf. Dr. Bekir DİZDAROĞLU
Co-LecturerNone
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
are how to solve partial diferantial equations especially in image processing.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : do the numerical solution of partial differantial equations1,4,81,3
PO - 2 : do the numerical solution of partial differantial equations1,5,81,3
PO - 3 : use finite difference and finite element methods for solving thePoisson equation.1,4,81,3
PO - 4 : use a variety of algorithms for solving the sparse linear systems.1,4,81,3
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome

 
Contents of the Course
Algorithms: Polynomials and Taylor series expansion. Ordinary differential equation: Numerical solutions, Difference schemes and Nonlinear ordinary differential equation. Partial differential equations and their discretization: Convection-diffusion equation, Stability analysis, Nonlinear equations and applications in image processing. Finite elements: Weak formulation, Linear finite elements, Unstructured finite-element meshes, Adaptive mesh refinement and High-order finite elements. Large sparse linear systems: Sparse matrices and their implementation, Iterative methods for large sparse linear systems and Parallelism.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Introduction
 Week 2Algorithms: Polynomials and Taylor series expansion.
 Week 3Algorithms: Polynomials and Taylor series expansion.
 Week 4Ordinary differential equation: Numerical solutions, Difference schemes and Nonlinear ordinary differential equation.
 Week 5Ordinary differential equation: Numerical solutions, Difference schemes and Nonlinear ordinary differential equation.
 Week 6Partial differential equations and their discretization: Convection-diffusion equation, Stability analysis, Nonlinear equations and applications in image processing.
 Week 7Partial differential equations and their discretization: Convection-diffusion equation, Stability analysis, Nonlinear equations and applications in image processing.
 Week 8Partial differential equations and their discretization: Convection-diffusion equation, Stability analysis, Nonlinear equations and applications in image processing.
 Week 9Mid-term exam
 Week 10Finite elements: Weak formulation, Linear finite elements, Unstructured finite-element meshes, Adaptive mesh refinement and High-order finite elements.
 Week 11Finite elements: Weak formulation, Linear finite elements, Unstructured finite-element meshes, Adaptive mesh refinement and High-order finite elements.
 Week 12Finite elements: Weak formulation, Linear finite elements, Unstructured finite-element meshes, Adaptive mesh refinement and High-order finite elements.
 Week 13Large sparse linear systems: Sparse matrices and their implementation, Iterative methods for large sparse linear systems and Parallelism.
 Week 14Large sparse linear systems: Sparse matrices and their implementation, Iterative methods for large sparse linear systems and Parallelism.
 Week 15Large sparse linear systems: Sparse matrices and their implementation, Iterative methods for large sparse linear systems and Parallelism.
 Week 16Final Exam
 
Textbook / Material
1Shapira, Y., 2006; Solving PDEs in C , SIAM, 508 papes.
 
Recommended Reading
1Salleh, S., Zomaya A.Y., Bakar, S. A., Computing for Numerical Methods Using Visual C , WILEY, 448 pages.
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 30/11/2023 2,0 50
End-of-term exam 17 18/01/2024 2,0 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 5 14 70
Arasınav için hazırlık 5 1 5
Arasınav 2 1 2
Ödev 10 1 10
Dönem sonu sınavı için hazırlık 5 1 5
Dönem sonu sınavı 2 1 2
Total work load136