The course objectives are:

A1: To understand and implement some basic numerical algorithms for computing solutions of single nonlinear equations and systems of linear equations.

A2: To understand and implement algorithms for interpolation, curve fitting, numerical differentiation, and numerical integration.

A3: To understand and implement numerical methods for solving ordinary differential equations, as well as elliptic and parabolic partial differential equations.

A4: Understand the key concepts of probability and Bayesian inference.

A5: Formulate and solve problems dealing with probability and statistics in civil and environmental engineering applications.

A6: Develop 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 targetted to civil and environmental engineering applications.

4. Monte Carlo simulation: theory and practice.

5. Introduction to Bayesian statistical inference with applications to the binomial and linear regression models.

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Content covered in course GENG1002 Introduction to Engineering Computations, and content covered in course MATH1120 Mathematics 2.

Internal Mode

Lecture
Tutorial

Lecture: for 4 hour(s) per Week for Full Term
Tutorial: for 2 hour(s) per Week for Full Term