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Swansea University College of Engineering Centenary Scholarship: New Generation of Real-Time Response Models Based on Deep Learning

Swansea University - Engineering

Qualification Type: PhD
Location: Swansea
Funding for: UK Students, EU Students
Funding amount: £14,296 per annum
Hours: Full Time
Placed On: 27th February 2019
Closes: 30th July 2019

Subject areas: Engineering

Project description: Complex large-scale dynamical system often has a vast number of degrees of freedom. It is not possible to model the system sufficiently quickly to predict behaviours in real time. This project will extract effective data to construct a fast model using deep learning methods. Deep learning is part of a broader family of machine learning methods and has been applied to a number of fields.

The proposed PhD project offers the unique opportunity to develop a data-driven reduced order model (ROM) using deep learning methods. It provides a fast way of performing computationally intensive tasks with real-time speed for fluids problem. The proposed new ROM is constructed from a number of simulations representing different parameters such as different initial or boundary conditions. The deep learning methods are used to extract features from those simulations. After constructing the ROM, it is able to run the simulations with several orders of magnitude speedup.

Project supervisor: Dr Dunhui Xiao


Candidates should hold a first class honours degree and/or distinction at Master’s level in Computational mathematics, statistics, computer science, physics, or engineering. 

Knowledge or experience of machine learning and fluid dynamics is an advantage (but not essential). Programming skills are essential.

We would normally expect the academic and English Language requirements to be met by point of application. For details on the University’s English Language entry requirements, please visit –

The scholarship covers the full cost of tuition fees and an annual stipend for UK/EU candidates but tuition fees only for international candidates.

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