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). This project has been co-defined with, and will be co-supervised by the Defence, Science and Technology Laboratory (Dstl).
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.
For informal technical enquires please contact Prof Leszek Gasieniec ([Email Address Removed])
For general application process queries contact Kelli Cassidy on [Email Address Removed]
To apply for this Studentship please follow the DA CDT Application Instructions. Submit an application for an Electrical Engineering PhD via the University of Liverpool’s online PhD application platform (https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/) and provide the project 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/distributed-algorithms-cdt/apply/
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