Computational Mathematics for Science and Engineering (MATH3101)

MATH3101 is a Mathematics Level III course. 

Units of credit: 6

Prerequisites: 12 units of credit in Level 2 Math courses including MATH2011 or MATH2111, and MATH2121 or MATH2221, or both MATH2019(DN) and MATH2089, or both MATH2069(CR) and MATH2099.

Exclusions: MATH3301 and MATH5305

Cycle of offering: Term 3 

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information: The course handout contains information about course objectives, assessment, course materials and the syllabus.

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its policy on plagiarism.

For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

The Online Handbook entry contains up-to-date timetabling information.

If you are currently enrolled in MATH3101, you can log into UNSW Moodle for this course.

MATH3101/5305 is designed to provide students with a solid mathematical foundation for employment or further study in a wide range of scientific and engineering fields that rely on numerical modeling based on partial differential equations.  The practical component of the course provides students with the opportunity to develop their programming skills.

Partial differential equations (PDEs) provide a natural mathematical description for many phenomena of interest in science and engineering. Such equations are often difficult or impossible to solve using purely analytical (pencil and paper) methods, especially for realistic industrial problems. This course introduces finite difference and finite element methods for elliptic and parabolic PDEs, and discusses key concepts such as stability, convergence and computational cost. Relevant techniques in numerical linear algebra are also discussed.

The course includes a substantial practical component dealing with the computer implementation of the algorithms used for solving partial differential equations.

Note: Students must have some prior experience with simple computer programming.