MATH5231 is available to Honours, Graduate Diploma and Masters programs in Mathematics, Statistics, Data Science, and Physical Oceanography.
Units of Credit: 6
Pre-requisite: 12 units of credit in Level 2 Maths courses including (MATH2501 or MATH2601) and (MATH2801 or MATH2901), or (both MATH2019/8 and MATH2089), or (both MATH2069 and MATH2099) or equivalent. Some computing experience (R, Fortran, Maple, Matlab, and/or Python) is strongly recommended.
Cycle of offering: T1 2023
Graduate attributes: The Course outline will be made available closer to the start of term - please visit this website: www.unsw.edu.au/course-outlines
This course is a graduate level overview of the mathematical foundations of inverse modelling and prediction and their application to real-world systems, primarily the ocean and atmosphere. The scientific emphasis is on the formal testing of models, formulated as rigorous hypotheses about the errors in all the information: dynamics, initial conditions, boundary conditions and data. Applications in meteorology, oceanography, and climate are presented in detail.
Refer to the course outline for information about course objectibves, assessment, course materials and the syllabus.
Important additional information as of 2023
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 information about the course. (The timetable is only up-to-date if the course is being offered this year).
If you are currently enolled in MATH5231, you can long into UNSW Moodle for this course.
This course aims to provide a graduate-level overview of the mathematical foundations of inverse modelling and prediction and their application to real-world systems, primarily the ocean and the atmosphere. The course introduces the fundamental mathematical underpinnings of forward and inverse modelling in the ocean and the atmosphere. The process of assimilating data into models using the calculus of variations is discussed, and the concept of over-determined and ill-posed problems is introduced.
A step-by-step development of maximally-efficient inversion algorithms, using ideal models, is complemented by computer codes and comprehensive details for realistic models. Variational tools and statistical concepts are concisely introduced, and applications to contemporary research models, numerical weather prediction, climate forecasting, and observing systems, are examined in detail.