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EPSRC Funded Project: Faster Uncertainty Quantification of Hydrocodes (EPSRC CDT in Distributed Algorithms)

   EPSRC CDT in Distributed Algorithms

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  Prof Leszek Gasieniec, Dr L Mason  No more applications being accepted  Funded PhD Project (European/UK Students Only)

About the Project

This PhD project is part of the University of Liverpool's Centre for Doctoral Training in Distributed Algorithms (CDT): The What, How and Where of Next-Generation Data Science.

The CDT works in partnership with STFC Hartree Centre and 20+ external partners from the manufacturing, defence and security sectors. Together they will provide a 4-year innovative PhD training course that will equip over 60 students with the essential skills needed to become future leaders in distributed algorithms, with the technical and professional networks needed to launch a career in next generation data science and future computing and the confidence to make a positive difference in society, the economy and beyond.

This project has been co-defined with, and will be co-supervised by the Defence, Science and Technology Laboratory (Dstl). This studentship is open to UK/EU students.

This EPSRC Centre for Doctoral Training in Distributed Algorithms (DA CDT) is offering a funded project focused on using machine learning to emulate computationally expensive calculations. The emulator can then be used to answer pertinent questions that are impossible to answer otherwise. The aim is to mirror successes that have been achieved using similar approaches in, for example, chemical formula formulation (where a two thirds reduction in computation required has been reported) and drug discovery (where a 95% reduction in the computational requirement needed for a certain objective has been reported).

The specific motivation relates to hydrocodes, high-fidelity (and highly optimised) simulations of fluid dynamics which involve computationally expensive calculations pertaining to the chemistry and physics involved. Individual simulations can take days, even with supercomputers. Were it possible to use historic simulations to learn to emulate the calculations involved, the emulator could then be used to perform offline sensitivity analyses with respect to, for example, the parameters of the chemistry and physics. Such sensitivity analyses are, at best, limited today, making it very challenging to identify opportunities to, for example, reduce the number of inputs to the hydrocode. Given the parameters are not known precisely, it is also desirable for any online use of the hydrocode to consider the uncertainty in those parameters in the calculation of any prediction. However, such Uncertainty Quantification (UQ) would demand multiple runs of the hydrocode. Given that it would be impossible to perform one simulation in an online setting, an emulator is a vital component of any online UQ.

While one could use, for example, a Gaussian Process (GP) to implement the emulator, it is not clear what kernel should be used, i.e. how any emulator should interpolate between the input-output pairs associated with the hydrocode. The statistical inference of the kernel is challenging, particularly in this setting, where there will be a need to interpolate between the information in the historic simulations and the prior knowledge (albeit incomplete and imprecise) of kernels implied by the knowledge of the physics and chemistry. Emerging numerical Bayesian inference algorithms (specifically Sequential Monte Carlo samplers) make it possible to capitalise on high performance computing without compromising the fidelity of that inference process.

The aim of this PhD is to take a specific hydrocode and to examine how these approaches can be used to expedite analysis.  The aim is to develop a single integrated approach to analysing and speeding up UQ on complex systems that is underpinned by a synergistic understanding of computer science and statistics. The anticipation is that this integrated approach would be sufficiently generic and transferable that it could be readily applied to other, similar problems.

This project is part of the EPSRC Funded CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.

Students will be based at the University of Liverpool and will be part of the CDT and Signal Processing  research community - a large, social and creative research group that works together solving tough research problems. Students have two academic supervisors and an industrial partner who provide co-supervision, placements and the opportunity to work on real world challenges. In addition, students attend technical and professional training to gain unparalleled expertise to make a difference now and in the future.

The CDT is committed to providing an inclusive environment in which diverse students can thrive. The CDT particularly encourages applications from women, disabled and Black, Asian and Minority Ethnic candidates, who are currently under-represented in the sector. We can also consider part time PhD students. We also encourage talented individuals from various backgrounds, with either an UG or MSc in a numerate subject and people with ambition and an interest in making a difference. 

The studentship is open to UK/EU students.

For informal technical enquires please contact Prof Leszek Gasieniec ([Email Address Removed])

For general application process queries contact [Email Address Removed]

Funding Notes

This project is a funded Studentship for 4 years in total and will provide UK tuition fees and maintenance at the UKRI Doctoral Stipend rate (£15,609 per annum, 2021/22).
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