By the end of this course students will be able to:

1. To understand and implement basic numerical algorithms for computing solutions of single nonlinear equations and systems of linear equations. To understand and implement the basics of the linear algebra.

2. To understand and implement algorithms for interpolation, curve fitting, numerical differentiation, and numerical integration.

3. To understand and implement numerical methods for solving parabolic and hyperbolic partial differential equations.

4. Demonstrate an understanding of the key concepts of probability and Bayesian inference.

5. Formulate and solve problems dealing with probability and statistics with engineering applications.

6. Demonstrate practical skill in Monte Carlo simulation

The content of the course includes:

1. The numerical solution of a single nonlinear equation, systems of linear equations, ordinary differential equations, and partial differential equations.

2. The numerical implementation of interpolation and curve fitting.

3. An introduction to probability and distribution theory targeted mainly to mechanical engineering applications.

4. Monte Carlo simulation: theory and practice.

5. Introduction to Bayesian statistical inference with applications to common engineering probability models

Not Applicable

Not Applicable

0

GENG1003 and MATH2310

Internal Mode

Lecture
Tutorial
Computer Lab

Tutorial: for 3 hour(s) per Week for Full Term
Lecture: for 3 hour(s) per Week for Full Term
Computer Lab: for 2 hour(s) per Week for Full Term