High-fidelity hydrocode simulations are very slow running computer codes that accurately model fluid dynamic systems. To perform Uncertainty Analysis using these simulations would require a large numbers of model runs. When inputs parameterising the operation of these codes are highly multidimensional and uncertain, the analysis becomes difficult and time consuming. This leads to the question of how to speed-up this Uncertainty Quantification (UQ), with the aim of making it possible to answer UQ questions in real-time contexts.
There are many approaches that could be adopted to undertake this speed-up. For example, careful experimental design could be used to optimise and/or reduce the dimensionality of the input and/or the number of model runs. Alternatively, careful examination of the simulation code could identify approximations / emulations that have minimal effect on the fidelity of output while dramatically reducing run-time. Similarly, careful re-factoring of the implementation could make speed-ups possible through efficient use of distributed computational resources. While these approaches could each result in speed-ups, it is unclear to what extent it is possible to combine their benefits to best effect.
The aim of this PhD is to take a specific hydrocode and to examine how these statistical and computational approaches (both in isolation and in combination) 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. https://www.liverpool.ac.uk/research/research-at-liverpool/research-themes/digital/cdt-distributed-algorithms/
The University of Liverpool is working in partnership with the STFC Hartree Centre and other industrial partners from the manufacturing, defence and security sectors to provide a 4 year innovative PhD training course that will equip over 60 students with the essential skills needed to become future leaders in data science, be it in academia or industry.
Every project within the centre is offered in collaboration with an Industrial partner who as well as providing co-supervision will also offer the unique opportunity for students to access state of the art computing platforms, work on real world problems, benchmarking and data. Our graduates will gain unparalleled experiences working across academic disciplines in highly sought-after topic areas, answering industry need.
As well as learning from academic and industrial world leaders, the centre has a dedicated programme of interdisciplinary research training including the opportunity to undertake modules at the global pinnacle of Data science teaching. A large number of events and training sessions are undertaken as a cohort of PhD students, allowing you to build personal and professional relationships that we hope will lead to research collaboration either now or in your future.
The learning nurtured at this centre will be based upon anticipation of the hardware resources arriving on desks of students after they graduate, rather than the hardware available today.
To apply for this Studentship please submit an application for an Electrical Engineering PhD via our online platform (https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/
) and provide the studentship title and supervisor details when prompted. Should you wish to apply for more than one project, please provide a ranked list of those you are interested in.
For a full list of the entry criteria and a recruitment timeline (including interview dates etc), Please see our website https://www.liverpool.ac.uk/research/research-at-liverpool/research-themes/digital/cdt-distributed-algorithms/
For informal enquires please contact Prof Leszek Gasieniec [email protected]
or [email protected]
Dr Luke Mason - STFC Hartree Centre
I’ve worked in computational science for 15 years and started by developing control software for embedded and robotic systems before moving to high performance computing (HPC) 10 years ago. I have worked on a diverse range of HPC software over the years, from models of high velocity impact to porting weather and climate models to new computing architectures. I currently lead the High Performance Software Engineering Group at the Science and Technology Facilities Council (STFC) Hartree Centre. We specialise in code scalability and performance on HPC systems, as well as porting and optimisation for emerging technologies and novel architectures.
I enjoy working alongside both industry and academic scientists to produce accurate and efficient code across a range of disciplines and architectures. This Centre for Doctoral Training offers an excellent opportunity to develop new algorithms, optimised for the latest hardware and accelerate their up take into industry.
My group is involved in a wide range of projects such as the Advanced Propulsion Centre funded DYNAMO project, led by Ford, which aims to reduce emissions from fleet internal combustion engines and the Programme for Advanced Computing in Europe (PRACE). We have had recent successes in the field of quantum computing and have a research collaboration with Atos to develop prototype applications.