| Unit name | AI Driven Design and Simulation |
|---|---|
| Unit code | CADEM0029 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Elsaied |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Civil, Aerospace and Design Engineering |
| Faculty | Faculty of Science and Engineering |
Why is this unit important?
The introduction of Artificial Intelligence (AI) into engineering design and simulation activities represents a paradigm shift in engineering practice. This unit empowers students to harness AI's transformative potential in automating design processes, accelerating simulations, and discovering innovative solutions beyond traditional approaches. As industries increasingly adopt AI-driven methodologies for product development and system optimization, mastering these techniques becomes essential for modern engineers. Students will learn to leverage cutting-edge AI tools, how to evaluate their accuracy and robustness, and how to apply them to relevant engineering problems.
How does this unit fit into your programme of study?
This unit combines computational methods, engineering principles, and AI technologies to prepare students for the future of engineering practice. For engineering students, it provides essential skills in AI-augmented design workflows and intelligent simulation techniques. For computer science students, it offers practical applications of AI in real-world engineering contexts. The unit builds on the foundational AI skills students develop in TB1, by introducing them to more advanced AI technologies and how to use them to solve engineering problems. Additionally, this unit prepares students for their group research project by ensuring they are equipped with the right AI tools to tackle engineering research tasks.
An overview of content
In this unit, students further develop their foundational AI knowledge gained in TB1 through application to engineering design and simulation problems. This unit explores the intersection of artificial intelligence with engineering design and simulation. You will master neural network architectures for design generation, including variational autoencoders and generative adversarial networks. The course covers physics-informed neural networks (PINNs) for solving differential equations, surrogate modelling, and Generative AI for design optimization. You'll work with real world design challenges, implementing AI solutions for optimization, design, and simulation acceleration.
How will students, personally, be different as a result of the unit
Upon completion, students will approach engineering design and simulation challenges equipped with AI tools that can explore vast design spaces efficiently. Students will be able to develop intelligent simulation frameworks that learn from data and accelerate computational analyses. Students will be able to critically evaluate when and how to apply AI in engineering contexts, enabling them to tackle complex multi-disciplinary design and analysis problems that traditional methods cannot solve effectively.
Learning Outcomes
On the successful completion of this unit, you will be able to:
The unit will be taught using a blend of synchronous and asynchronous activities, including lectures, guided computer sessions, drop-in sessions, and self-directed exercises. A critical aspect of the unit's success is the practical experience gained in developing machine learning (ML) algorithms using Python, alongside the presentation and discussion of these algorithms during lectures.
Tasks which help you learn and prepare you for summative tasks (formative):
Weekly practical computer session on applied machine learning will be held to complement the taught lectures. Students will keep a portfolio of the tasks completed during these sessions, which will contribute towards the summative individual portfolio. Regular feedback will be given on the students’ portfolios throughout the term.
Tasks which count towards your unit mark (summative):
[100%] – individual portfolio (ILO 1, 2, 3, 4)
This portfolio will consist of the tasks completed during the lab sessions, and an individual unit project technical report on the use of machine learning techniques in engineering applications.
When assessment does not go to plan
The coursework and portfolio are unchanged for re-assessment; Students will revise and resubmit their work.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. CADEM0029).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
