EE2108Computational Engineering Analysis

What you will study

Course overview and aims

The course aims to analyze the concept of numeric methods and their application in engineering problem solving. Students will evaluate how to formulate numerical models, select suitable numerical methods, and implement them using computer programming language, such as Python, MATLAB, etc. The course will examine the relationship between numeric methods and engineering problem solving, enabling students to apply these methods effectively.

Keyword syllabus

Introduction Overview of numerical analysis, programming fundamentals, data visualization, error analysis Root Searching Techniques Bracketing methods, open methods Linear Systems Matrix algebra, Gauss elimination, LU factorization, Gauss-Seidel method Curve Fitting Least-squares data fitting, polynomial interpolation, splines Differentiation and Integration Numerical differentiation, Trapezoidal rule, Simpson’s rules, Romberg integration

Official PDF, page 4 · Keyword Syllabus

Intended learning outcomes

View learning outcomes

CILO	Intended learning outcome	Weight	DEC
1	Apply numerical methods for solving engineering and mathematical modelling problems	—	DEC-A1, DEC-A2
2	Analyze different types of errors in numerical computation	—	DEC-A1, DEC-A2
3	Develop computer programs to solve numerical problems	—	DEC-A1, DEC-A2

Official PDF, page 2 · Course Intended Learning Outcomes (CILOs)

Before you take this course

Check prerequisites, precursors and corequisites separately, together with equivalence and exclusion conditions. “and” means conditions must be met together; “or” marks alternatives. Read combined conditions in the groupings shown in the original text below.

Prerequisites

For Normative 4-year Degree Students Only: MA1200 Calculus and Basic Linear Algebra I or MA1300 Enhanced Calculus and Linear Algebra I

Precursors

For Normative 4-year Degree Students Only: MA1201 Calculus and Basic Linear Algebra II or MA1301 Enhanced Calculus and Linear Algebra II

Corequisites

This edition of the official syllabus does not state this item.

Additional course requirements

This edition of the official syllabus does not state this item.

Equivalent courses and conditions

Nil

Exclusive courses and conditions

Nil

Assessment and passing the course

Assessment

Continuous assessment: 50%; Examination: 50%; Examination duration: 2 hours

Assessment breakdown

Assessment task	Weight	Generative AI	Remarks and restrictions
TestsCILO 1, 2, 3	30%	No	-
AssignmentsCILO 1, 2, 3	10%	Yes	-
Lab Exercises/ReportsCILO 1, 2, 3	10%	Yes	-

Official PDF, pages 2–3 · Assessment Tasks / Activities (ATs)

View the original assessment table and instructions

Assessment Tasks / Activities (ATs)

3      EE2108: Computational Engineering Analysis




                        ATs                  CILO No.        Weighting (%)        Remarks ("-" for    Allow Use of
                                                                                  nil entry)          GenAI?
1                       Tests (min.: 2)      1, 2, 3         30                   -                   No
2                       #Assignments         1, 2, 3         10                   -                   Yes
                        (min.: 3)
3                       Lab Exercises/       1, 2, 3         10                   -                   Yes
                        Reports

Passing requirements

Remark: To pass the course, students are required to achieve at least 30% in course work and 30% in the examination. Also, 75% laboratory attendance rate must be obtained. # may include homework, tutorial exercise, project/mini-project, presentation