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Using Machine Learning to Simulate Macroscopic phenomena for Fluid Dynamics

   Computer Science

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Macroscopic dynamic phenomena involving free and solid boundaries exist both in nature and industrial environments and are often associated with incidents of potentially high industrial impact. Such dynamic phenomena are also at the core of numerous applications in immersive technologies (AR/VR/XR), computer graphics as well as in computational physics. Although various fluid-flow dynamics in the physical world have been studied extensively, methods to capture the complicated non-linear micro-mechanical phenomena have received less attention. The simulation of this kind of problem is a difficult and computationally demanding process.

The objective of this PhD research study is to develop efficient algorithms to simulate such a macroscopic phenomenon for fluid dynamics using machine learning techniques. During this project, you will have opportunities to gain experience with industry-demanding machine-learning technologies.

This PhD research study will be within the Intelligent Data Analysis Research Group (IDA). For over a decade, the IDA Research Group has been working in an extensive area of artificial intelligence and machine learning science. Recent projects include fluid dynamics, medical imaging, natural language processing, pattern recognition, and computer graphics. We have received numerous research awards from international conferences, research institutions, and professional organisations.

Research journey

Doctoral research programmes (PhDs) take a proud place in the world-class research environment and community at Brunel. PhD students are recognised and valued by their supervisors as an essential part of their departments and a key component of the university's overall strategy to develop and deliver world-class research.

A PhD programme is expected to take 3 years full-time or 6 years part-time, with intakes starting in January, April or October.

The general University entrance requirement for registration for a research degree is normally a First or Upper Second Class Honours degree (1st or 2:1) or an international equivalent. A Masters degree is a welcome, but not required, qualification for entry.

Find out how to apply for a PhD at Brunel

Research support

Excellent research support and training

The Graduate School provides a range of personal, professional and career development opportunities. This includes workshops, online training, coaching and events, to enable you to enhance your professional profile, refine your skills, and plan your next career steps as part of the Researcher Development Programme. The researcher development programme (RDP) offers workshops and seminars in a range of areas including progression, research management, research dissemination, and careers and personal development. You will also be offered a number of online, self-study courses on BBL, including Research Integrity, Research Skills Toolkit, Research Methods in Literature Review and Principles of Research Methods.

Library services

Brunel's Library is open 24 hours a day, has 400,000 books and 250,000 ebooks, and an annual budget of almost £2m. Subject information Specialists train students in the latest technology, digital literacy, and digital dissemination of scholarly outputs. As well as the physical resources available in the Library, we also provide access to a wealth of electronic resources. These include databases, journals and e-books. Access to these resources has been bought by the Library through subscription and is limited to current staff and students. 

Dedicated research support staff provide guidance and training on open access, research data management, copyright and other research integrity issues.

Find out more: Brunel Library

Careers support

You will receive tailored careers support during your PhD and for up to three years after you complete your research at Brunel. We encourage you to actively engage in career planning and managing your personal development right from the start of your research, even (or perhaps especially) if you don't yet have a career path in mind. Our careers provision includes online information and advice, one-to-one consultations and a range of events and workshops. The Professional Development Centre runs a varied programme of careers events throughout the academic year. These include industry insight sessions, recruitment fairs, employer pop-ups and skills workshops.


Jonathan F. MacArt, Justin Sirignano, Marco Panesi, AIAA SCITECH 2022 Forum, (2022).
Vinuesa, R., Brunton, S.L. Enhancing computational fluid dynamics with machine learning. Nat Comput Sci 2, 358–366 (2022).

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Research output data provided by the Research Excellence Framework (REF)

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