Ability for deep understanding of the fundamental numerical methods.
Ability to recognize the advantages and disadvantages of each method in order to decide the most convenient in use on application basis
Ability to use specific software in order to develop the necessary applications
Ability to analyze and interpret data
There are no prerequisite modules. It is, however, recommended that students should have a good knowledge of Mathematics (Calculus, Linear Algebra, Differential Equations) as well as fundamental skills on Scientific Programming)
Introduction (discretization, error analysis), Numerical Differentiation (forward, backward and central differences), Numerical Integration (trapezoid rule, Simpson rule, Newton-Cotes formulae), Interpolation/Extrapolation (Taylor, Lagrange polynomials), Numerical solution of algebraic equations (trial & error, bisection, Newton-Raphson), Numerical solution of linear systems (Gauss, Jacobi, Gauss-Seidel), Numerical Integration of Ordinary Differential Equations (Euler, Runge-Kutta), Finite Differences, Special Topics, Non-linear systems.
LECTURES: 3 h/w
RECITATION: 1 h/w
LAB/PRACTICE: 3 h/w
Total Module Workload (ECTS Standards):
1. Laboratory problem-solving by the students (35% of the final grade).
2. Written examination (open-book, 65% of the final grade).
1. Chapra S. & Canale R., “Numerical Methods for Engineers” (6th ed.), McGraw-Hill (2012)
2. Pozrikidis C., “Numerical Computation in Science and Engineering”, Oxford University Press, New York (1998).
3. Daoutidis P., Mastrogeorgopoulos, S. & Sidiropoulou, E. “Numerical Methods for engineering problems”, Anikoula Ed., Thessaloniki (2010), in Greek.