MAS316 Mathematical modelling of natural systems
|Semester 2, 2021/22||10 Credits|
|Lecturer:||Dr Ashley Willis||Home page||Timetable||Reading List|
Mathematical modelling enables insight in to a wide range of scientific problems. This module will provide a practical introduction to techniques for modelling natural systems. Students will learn how to construct, analyse and interpret mathematical models, using a combination of differential equations, scientific computing and mathematical reasoning. Students will learn the art of mathematical modelling: translating a scientific problem into a mathematical model, identifying and using appropriate mathematical tools to analyse the model, and finally relating the significance of the mathematical results back to the original problem. Study systems will be drawn from throughout the environmental and life sciences.
Prerequisites: MAS212 (Scientific Computing and Simulation) Please contact lecturers if you did not take this module; MAS222 (Differential Equations)
No other modules have this module as a prerequisite.
- An introduction to chaotic dynamics.
- Machine learning methods to retrieve forest biophysical parameters.
- A stochastic approach to modelling disease spread.
- develop students’ skills in comprehending problems, formulating them mathematically and obtaining solutions by appropriate methods;
- provide practical demonstrations of how mathematical modelling may be used to gain insight in to the dynamics of natural systems;
- build on mathematical methods (ordinary/partial differential equations, linear stability analysis, scientific computing in Python) learned at earlier levels, and expose students to how they can be used to model natural systems.
- students can formulate problems mathematically and obtain solutions by appropriate methods;
- students can describe how mathematical modelling may be used to gain insight in to the dynamics of natural systems;
- students can build on mathematical methods (ordinary/partial differential equations, linear stability analysis, scientific computing in Python) learned at earlier levels to model natural systems.
12 lectures, no tutorials, 9 practicals
Three pieces of coursework, with no examination.
|B||Hastie T, Tibshirani R, Friedman JH||The Elements of Statistical Learning: Data Mining, Inference, and Prediction||006.31 (H)||Blackwells||Amazon|
|B||Murray JD||Mathematical Biology I and II||570.15118 (M)||Blackwells||Amazon|
|B||Strogatz SH||Nonlinear dynamics and chaos: with applications to physics, physics, biology, chemistry, and engineering||531.3 (S)||Blackwells||Amazon|
(A = essential, B = recommended, C = background.)
Most books on reading lists should also be available from the Blackwells shop at Jessop West.
|Tue||12:00 - 12:50||lecture||Blackboard Online|
|Tue||16:00 - 16:50||lab session||Blackboard Online|