Computational Fluid Dynamics (CFD) is increasingly used to analyze the hydrodynamic features of the built and natural environment; from warming ocean currents to high-speed ocean liners. However, the computational cost of such simulations is still too high for engineering purposes without making simplifying assumption which greatly limit the accuracy.
Machine Learning appears to be the panacea of our time, but it does little to address this issue on its own. Most state-of-the-art machine learning methods require many thousands or millions of examples to learn from, which is typically impossible for large scale hydrodynamic problems.
In partnership with the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence, this project will develop new and useful methods for integrating data-centric approaches such as machine learning directly into CFD simulations and analysis, with the goal of reducing their cost while increasing their accuracy.
If you wish to discuss any details of the project informally, please contact Gabriel Weymouth, FSI Research Group, Email: G.D.Weymouth@soton.ac.uk.
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date: applications should be received no later than 31 August 2020 for standard admissions, but later applications may be considered depending on the funds remaining in place.
Funding: full tuition plus, for UK students, an enhanced stipend of £15,009 tax-free per annum for up to 3.5 years.
How To Apply
Applications should be made online selecting “PhD Engineering and Environment (Full time)” as the programme. Please enter Gabe Weymouth in section 2, Proposed Supervisor.
Applications should include:
- Curriculum Vitae
- Two reference letters
- Degree Transcripts to date
For further information please contact: email@example.com