15 Credits SPRING



Aims/Description: This module will provide a practical introduction to techniques used for modelling and simulating dynamic natural systems. Many natural systems can be modelled appropriately using differential equations, or individual based methods. In this module, you will explore and understand both modelling approaches. You will gain knowledge of the assumptions underlying these models, their limitations, and how they are derived. You will learn how to simulate and explore the dynamics of computational models, using a variety of examples mostly drawn from natural systems.  At the end of the module, we will introduce basic recurrent neural networks as examples of dynamical systems with multiple timescales.

Restrictions on availability: Prerequisites for this module are programming proficiency (preferably in MATLAB or Python) and A-level Mathematics (or equivalent).

Staff Contact: MANNESCHI LUCA
Teaching Methods: Lectures, Tutorials, Laboratory work, Independent Study
Assessment: Formal Exam, Project/ portfolio

Notes: This unit forms part of an accredited degree programme

Information on the department responsible for this unit (Computer Science):

Departmental Home Page
Teaching timetable

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NOTE
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.

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Teaching methods and assessment displayed on this page are indicative for 2025-26.

Western Bank, Sheffield, S10 2TN, UK