# Search Course Outline

### 5 course outlines found

1

#### MATHS 363

: Advanced Computational Mathematics2024 Semester One (1243)

Finite element methods, calculus of variations and control theory are key mathematical tools used to model, compute approximations to model solutions and to understand the control of real-world phenomena. These topics share the same mathematical foundations and can all be described as variational methods. The course offers advanced techniques to handle complicated geometries and optimise desired objectives in applications modelled using differential equations. Recommended preparation: MATHS 253

Prerequisite: MATHS 260 and 270

2

#### MATHS 363

: Advanced Modelling and Computation2023 Semester One (1233)

In real-world situations, the interesting and important variables are often not directly observable. To address this problem, mathematical models and quantities that are observable are usually employed to carry out inference on the variables of interest. This course is an introduction to fitting of models to (noisy) observational data and how to compute estimates for the interesting variables. Numerical methods for partial differential equations, which are commonly used as models for the observations, will also be covered.

Prerequisite: MATHS 260 and 270

3

#### MATHS 363

: Advanced Modelling and Computation2022 Semester One (1223)

In real-world situations, the interesting and important variables are often not directly observable. To address this problem, mathematical models and quantities that are observable are usually employed to carry out inference on the variables of interest. This course is an introduction to fitting of models to (noisy) observational data and how to compute estimates for the interesting variables. Numerical methods for partial differential equations, which are commonly used as models for the observations, will also be covered.

Prerequisite: MATHS 260 and 270

4

#### MATHS 363

: Advanced Modelling and Computation2021 Semester One (1213)

In real-world situations, the interesting and important variables are often not directly observable. To address this problem, mathematical models and quantities that are observable are usually employed to carry out inference on the variables of interest. This course is an introduction to fitting of models to (noisy) observational data and how to compute estimates for the interesting variables. Numerical methods for partial differential equations, which are commonly used as models for the observations, will also be covered.

Prerequisite: MATHS 260 and 270

5

#### MATHS 363

: Advanced Modelling and Computation2020 Semester One (1203)

Prerequisite: MATHS 260 and 270