Be able to perform simple programming tasks using any platform (preferably Octave)

Homework, Projects, and all other assignments will be submitted in electronic format

No homework assignments need to be submitted, they are just for personal practice and solutions will be provided.

Assessment Tools

T1: Examinations/Tests
T3: Group Projects
T6: Student Survey

Course Objectives

The course will develop numerical methods aided by technology to solve algebraic, transcendental, and differential equations, and to calculate derivatives and integrals. The course will also develop an understanding of the elements of error analysis for numerical methods and certain proofs. The course will further develop problem solving skills.

Course Intended Learning Outcomes

By the end of this course, the student will be able to:

ILO#

Description

Assessment
Tool

Program
ILO

1

solve an algebraic or transcendental equation using an appropriate numerical method

T1,T6

1,5

2

prove results for numerical root finding methods

T1,T6

1

3

approximate a function using an appropriate numerical method

T1,T6

1,5

4

calculate a definite integral using an appropriate numerical method

T1,T6

1,5

5

solve a differential equation using an appropriate numerical method

T1,T6

1,5

6

evaluate a derivative at a value using an appropriate numerical method

T1,T6

1,

7

find optimal values that satisfy linear and nonlinear optimization problems

T1,T6

1,5

8

code a numerical method in a modern computer language

T3

5,11

9

Coordinate working in a multifunctional team

T3

4,10

10

Prepare and present technical reports

T3

7,11

References

Numerical Methods for Engineers, Raymond P. Canale and Steven C. Chapra, 6th ed, 2010, McGraw Hill, ISBN 978–0–07–340106–5

30% Final (Have to score at least 50% in final to pass)
20% Midterms (2 midterms, all counted)
10% Pop quizzes (3 to 8 randomly distributed – all counted)
40% Project

Course Project

Topics

The topics should include the solution of an initial-boundary value problem (PDE), optimization using genetic algorithms or neural networks, or dynamic finite element analysis of a problem.

All topics should include the creation of software for analysis of the collected data based on open source packages or compilers (penalty up to 10% for use of ready-made analysis software, and another 10% for using non open source software or platform)

Teams

The team should include four to eight students who will divide themselves into sub-teams working on different software functions’ development.

Project Evaluation

Project management (15%) (3 5-minute presentations by team coordinators)

Planning (40%)

Follow up (20%)

Corrective actions and re-planning (40%)

Final Report (40%)

Technical report format (Cover, TOC, Introduction and literature survey, mathematical background, results, conclusions, reference, appendices) (25%)

Rigor of literature survey (25%)

Details of the model derivation and development (25%)

Numerical results and verification (25%)

Software (25%)

Ease of use and operability (30%)

Accuracy (40%)

Output graphics (40%)

Final presentation (20%)

Public decimation of knowledge (Website. Wiki, videos, public slides, public reports, design graphics and charts, etc...) (25%)

Presentation graphics and appeal (15%)

Accuracy of presentation content (15%)

Comprehensibility of presentation content (15%)

Audience capturing (10%)

Audience participation and reply to questions (10%)

## Important Requirements

All students are required to## Assessment Tools

T1: Examinations/TestsT3: Group Projects

T6: Student Survey

## Course Objectives

The course will develop numerical methods aided by technology to solve algebraic, transcendental, and differential equations, and to calculate derivatives and integrals. The course will also develop an understanding of the elements of error analysis for numerical methods and certain proofs. The course will further develop problem solving skills.## Course Intended Learning Outcomes

By the end of this course, the student will be able to:Tool

ILO

## References

## Topics and Schedule

9/2/2017

Errors

16/2/2017

23/2/2017

2/3/2017

9/3/2017

Midterm #116/3/2017

23/3/2017

30/3/2017

Initial Value Problems – Euler, Runge-Kuta

6/4/2017

13/4/2017

Midterm #220/4/2017

27/4/2017

4/5/2017

11/5/2017

18/5/2017

## Assessment

30% Final (Have to scoreat least 50% in finalto pass)20% Midterms (2 midterms, all counted)

10% Pop quizzes (3 to 8 randomly distributed – all counted)

40% Project

## Course Project

## Topics

## Teams

The team should include four to eight students who will divide themselves into sub-teams working on different software functions’ development.## Project Evaluation

## Octave/FreeMat Tutorials

## Some Useful Material

Numerical methods for_engineers_for_engineers_chapra_canale_6th_editionfromNanda KishoreChapra applied numerical methods matlab engineers scientists 3rd txtbkfromKaXio Sosa