Course : MRES.A.02 Scientific Computing and Mathematical Modeling

Course code : REEE102

Course Description

This is the image of course

This course module offers advanced knowledge and skills in the ‘horizontal’ or across-areas subject of scientific computing and mathematical modeling. Such methods and tools are expected to be of value and immediate usefulness to all students, regardless of the specific specialization area selected by each student.

  • Course Syllabus

    • Mathematical Modeling
    • Introduction to Scientific Programming (S.P.), Modern S.P. Environments. Computer Errors
    • Numerical Linear Algebra in S.P. environments
    • Methodologies of approximation of functions and scientific data in S.P. environments.
    • Optimization Methodologies in S.P. Environments
    • Differentiation, Integration, Differential Equations
    • Introduction of parallel computation in modern S.P. Environments

    Course Objectives/Goals

    Students who successfully complete the Scientific Computing and Mathematical Modeling course

    • will understand basic scientific programming methodologies for solving mathematical problems.
    • will be able to implement solutions using the capabilities provided by modern scientific programming environments rather than programming them from scratch.
    • after understanding the mathematical nature of the problem that he/she will be asked to solve, will be able to determine its parameters and address the solution using tools provided by modern scientific programming environments.

    Prerequisites/Prior Knowledge

    Undergraduate courses on Mathematical Analysis

    • A course on Introduction to Linear Algebra
    • A course on programming (Matlab, Python, Julia, R, …)
    • A course on Numerical Analysis (optional).

    Bibliography

    • Numerical Analysis, Burden R., Faires J. D, Brooks\Cole.
    • A First Course in Numerical Analysis, A. Ralston, Ph. Rabinowitz, Mc Graw Hill.
    • Numerical Methods using Matlab, J. Mathews, K. Fink, Pearson Prentice Hall.
    • Applied Numerical Analysism C. Gerald, P. O. Wheatley, Addison Wesley.
    • Applied Numerical Analysis Using Matlab, L. Fausett, Pearson Prentice Hall.
    • Numerical Methods for Engineers, With Software and Programming Applications Fourth Edition, S.C. Chapra, R.P. Canale , MC Geaw Hill, 2002
    • Numerical Python, Scientific Programming and Data Science Applications with Numpy, Scipy and Matplotlib, R. Johansson, Apress
    • Practical Numerical and Scientific Computing with MATLAB and Python”, 1st edition, Eihab B. M. Bashie , CRC Press “
    • Learning Scientific Programming with Python, Christias Hill

    Assessment Methods

    Student evaluation comes from

    • Class participation and contribution in the discussions held in class and online x 20%
    • Average Grade of Homework Assignments (best 4 out of the total of 5 grades obtained) x 40%
    • Final written exam on computer x 40%

    Additional info

    Relative Scientific Journals:

     

    • SIAM Journal on Numerical Analysis
    • International Journal for Numerical Methods in Engineering
    • Applied Numerical Mathematics
    • Journal of Computational and Applied Mathematics
    • Numerical Algorithms
    • Numerische Mathematik
    • Scientific Programming

     

     

    TOOLS

     

    WEBSITES

Units

Agenda

Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
29
30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
Due day
Course event
System event
Personal event

Announcements

All announcements...
  • - There are no announcements -