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Using Machine Learning to Simulate Macroscopic phenomena for Fluid Dynamics

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.

References

  • Jonathan F. MacArt, Justin Sirignano, Marco Panesi, AIAA SCITECH 2022 Forum, (2022). https://doi.org/10.2514/6.2022-1703.
  • Vinuesa, R., Brunton, S.L. Enhancing computational fluid dynamics with machine learning. Nat Comput Sci 2, 358–366 (2022). https://doi.org/10.1038/s43588-022-00264-7.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)


Nadine Aburumman - Nadine is a lecturer at the Computer Science Department, and a member of the Intelligent Data Analysis (IDA), the Interactive Multimedia System (IMS) and Human-Computer Interaction (HCI) research groups. She is doing research in computer graphics, physics-based animation, real-time physics, skinning, secondary motions, cloth simulation and fluid simulation. She has focused her career both on computer science and digital arts. The ultimate goal of her research is to improve the quality of 3D animated movies, video games and VR/AR/XR through physically based simulations of virtual characters, cloth, soft bodies, fluids and rigid bodies. Towards this goal, she intends to advance computer games and computer animation on various levels, mainly through efficient physics-based algorithms, skeletal based deformation, and machine learning.

Related Research Group(s)

Intelligent Data Analysis

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.