# NUMERICAL SIMULATIONS & MACHINE LEARNING - 2023/4

Module code: ENG3209

## Module Overview

The FHEQ Level 6 treatment of numerical methods builds on the material taught at FHEQ Level 5. It is presented in two linked sections: Numerical Simulations and Machine Learning. The Numerical Simulations section discusses typical methods used in engineering simulations to obtain numerical solutions to real-world problems described by ordinary and partial differential equations. Students apply their programming skills acquired at FHEQ Level 5 to use numerical methods for the solution of engineering problems. The Machine Learning section introduces concepts from artificial intelligence relevant for engineers. It provides an overview and discussion of machine-learning techniques, and students apply these techniques to solve data-driven engineering problems. A laboratory session is used to explore the concepts of uncertainty, verification and validation for computer simulations.

### Module provider

Mechanical Engineering Sciences

MARXEN Olaf (Mech Eng Sci)

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

Independent Learning Hours: 100

Lecture Hours: 10

Tutorial Hours: 11

Laboratory Hours: 4

Guided Learning: 11

Captured Content: 14

Semester 2

## Prerequisites / Co-requisites

ENG2124 Numerical Methods & Applied Programming or ENG2093 Numerical & Experimental Methods

## Module content

Indicative content includes:

Numerical simulations:

• Overview of numerical solution of engineering problems, workflow for simulation methods and numerical modelling.

• Stability and accuracy of integration methods for ordinary differential equations; Implementation of explicit and implicit methods for the integration of ordinary differential equations.

• Finite difference methods: derivation based on Taylor-series expansion; finite difference approximation for the first and second derivatives and their accuracy; the concept of modified wave number, von Neumann and Fourier analysis.

• Partial differential equations: order, linearity and classification (elliptic, parabolic and hyperbolic equations).

• Analysis of numerical schemes: consistency, stability and convergence; Lax' equivalence theorem.

• Methods and combined analysis of spatio-temporal discretization for partial differential equations; amplitude and phase errors including numerical dispersion and diffusion; the convection-based CFL number and corresponding diffusion number.

• Verification and validation of numerical simulations.

Machine Learning:

• Introduction to machine learning for engineering; machine-learning workflow and model selection;

• Supervised and unsupervised learning;

• Regression, clustering and classification;

• Deep-learning methods and neural networks;

• Physics-informed machine learning;

• Application of machine learning to engineering problems.

## Assessment pattern

Assessment type Unit of assessment Weighting
Practical based assessment Laboratory Session Pass/Fail

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## Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate command of a variety of numerical methods and machine-learning techniques as well as the ability to select appropriate methods, and then use these methods and techniques to obtain numerical solutions to engineering problems, including the analysis of errors.

The practical based assessment tests the students ability to create numerical model(s) for one or more simple engineering problem(s), perform (in person) and/or observe (virtual) the associated laboratory experiment(s) and compare results, including an analysis of results and discussion of errors as well as uncertainties (verification and validation).

The coursework assignment tests the students ability to select and implement a suitable combination of numerical methods and machine-learning techniques, and then to apply the resulting simulation tool to solve a complex engineering problem, including the presentation of outcomes in a written report.

Thus, the summative assessment for this module consists of: Practical based assessment [Learning outcomes 1,2] and simulation task assignment [Learning outcome 3].

Formative assessment and feedback:

Formative feedback is given throughout the semester during Q&A sessions as part of lectures by staff. Formative feedback is also given throughout the semester in IT-Lab based tutorials by staff and/or PG assistants, and through example demonstrations and computer codes posted on the VLE. In the laboratory session, students have a face-to face discussion with the demonstrator. Written feedback is given on the coursework assignment.

## Module aims

• Knowledge and experience of analysis and selection of numerical and machine-learning methods for complex engineering problems .
• Knowledge and experience of implementation and application of appropriate computer-based programming methods to solve ordinary and partial differential equations governing complex engineering problems.
• Knowledge and experience of taking all steps to simulate complex engineering problems using numerical and machine-learning methods, to critically assess the validity of solutions and to quantify errors of computer simulations for complex engineering problems.

## Learning outcomes

 Ref Attributes Developed 001 Ability to analyze and implement computer-based numerical methods and/or machine-learning techniques to solve a simple engineering problem; KC C1,C2 002 Ability to produce and assess results of computer-based and (real-world) experimental methods for a simple engineering problem, including verification and validation; CPT C3,C12 003 Develop, select and implement a suitable approach for the computer simulation of a complex engineering problem, then perform simulations and critically analyze results as well as communicate corresponding findings; KCPT C1,C2,C3,C17

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

## Methods of Teaching / Learning

The learning and teaching strategy is designed to introduce numerical simulation techniques and machine learning through theory with worked examples carried out by the students. This is delivered through synchronous lectures, captured content and tutorial classes with the students conducting a coursework assignment as well as a single laboratory session.

The learning and teaching methods include:

• weekly synchronous lectures, including question and answer session;

• pre-recorded captured content;

• guided learning (such as electronic/online learning and multi-media resources);

• IT-lab based tutorials, were practical programming skills are developed through several formative exercises.

• a 2 hours laboratory session in small groups; the lab session may feature supervisor-led discussions and group work to conduct an experiment itself, and it will also require preparatory wotk.

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.

Upon accessing the reading list, please search for the module using the module code: ENG3209

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## Programmes this module appears in

Programme Semester Classification Qualifying conditions
Automotive Engineering (Dual degree with HIT) BEng (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Aerospace Engineering BEng (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Mechanical Engineering BEng (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Automotive Engineering BEng (Hons) 2 Optional A weighted aggregate mark of 40% is required to pass the module
Automotive Engineering MEng 2 Optional A weighted aggregate mark of 40% is required to pass the module
Aerospace Engineering MEng 2 Optional A weighted aggregate mark of 40% is required to pass the module
Mechanical Engineering MEng 2 Optional A weighted aggregate mark of 40% is required to pass the module

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 2023/4 academic year.