NUMERICAL AND STATISTICAL METHODS - 2027/8

Module Overview

Civil Engineers routinely make use of software tools for calculations on physical systems, ranging from structural analysis, to soil mechanics and fluid dynamics. Increasingly, Civil Engineers are also utilising generative AI-based tools and machine learning methods to accelerate their workflows. This module provides an introduction to the numerical and statistical methods underlying many of these tools, including Finite Element and Finite Difference Methods as well as regression and artificial neural networks.

The module is hands-on: students will be introduced to MATLAB and learn to write their own programs, supported through the critical use of Copilot, to apply the methods encountered in the module.

Module provider

School of Engineering

Module Leader

KREITMAIR Monika (Sch of Eng)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

The module sits within the core subject of mathematic and covers the following areas:

• Fundamental programming concepts: variables, functions, control structures, vector and matrix data structures
• Syntax, semantics and good programming practice
• Using the MATLAB programming language and integrated development environment
• Using MATLAB Copilot to develop code, and critically interpreting the resulting output
• Solution of Ordinary Differential Equations by Runge-Kutta and associated methods
• Solution methods for simultaneous linear equations
• Principles and application of the Finite Difference Method for solving Ordinary and Partial Differential Equations
• Principles and application of the Finite Element Method applied on truss structures
• Principles and application of statistical methods, including regression and artificial neural networks for predictive modelling

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

Summative assessment

The summative assessment for this module consists of a class test and an exam. In the class test, students demonstrate their understanding of programmatic code structure and logic, including the use of functions, variables, matrix and vector data structures, control structures and artificial neural network creation (learning outcomes 2 to 5). The end-of-semester exam assesses theoretical understanding of numerical and statistical methods and programming practice (learning outcomes 1, 3, 5).

Formative assessment and feedback

During computer lab-based tutorials students have an opportunity to receive verbal feedback on their work. Additionally, students can test their understanding and get immediate feedback through formative assessment in the form of weekly multiple-choice tests. Students are required to submit their tutorial attempts, at least 60% to pass, for further formative feedback.

Module aims

• Knowledge and experience of the use of standard numerical and statistical methods to solve complex engineering problems
• Knowledge and experience of using computer programming as a tool to solve engineering problems
• Knowledge and experience of effective and critical use of AI-based tools for computer programming to solve engineering problems
• Understanding of basic machine learning concepts, specifically regarding predictive modelling

Learning outcomes

Methods of Teaching / Learning

The teaching uses weekly lectures to introduce key concepts and theoretical background. Students develop skills in applying the concepts and theory in weekly exercises that bring the newly learned concepts and knowledge to practice. Weekly tutorial sessions are used to provide feedback and discuss these exercises. The initial stage of the module is dedicated to programming fundamentals. In this period the tutorials are longer than usual (two hours instead of one) because of the importance of feedback in this stage of learning. Captured content is used for brief summaries of key concepts and for step-by-step examples.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ENG2106

Other information

Surrey's Curriculum Framework is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills and capabilities in the following areas:

Digital Capabilities: As a module with a strong focus on numerical methods and computer programming, the development of Digital Capabilities is central.

Resourcefulness and Resilience: The in-semester test requires proactive engagement with a provided piece of code to develop understanding of code structure. This emphasis on the development of skills more than factual knowledge resonates well with the Resourcefulness and Resilience pillar of the framework.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.