The achievements of modern research and their rapid progress from theory to application are increasingly underpinned by computation. Computational approaches are often hailed as a new third pillar of science - in addition to empirical and theoretical work. While its breadth makes computation almost as ubiquitous as mathematics as a key tool in science and engineering, it is a much younger discipline and stands to benefit enormously from building increased capacity and increased efforts towards integration, standardization, and professionalism. The development of new ideas and techniques in computing is extremely rapid, the progress enabled by these breakthroughs is enormous, and their impact on society is substantial: modern technologies ranging from the Airbus 380, MRI scans and smartphone CPUs could not have been developed without computer simulation; progress on major scientific questions from climate change to astronomy are driven by the results from computational models; major investment decisions are underwritten by computational modelling. Furthermore, simulation modelling is emerging as a key tool within domains experiencing a data revolution such as biomedicine and finance. This progress has been enabled through the rapid increase of computational power, and was based in the past on an increased rate at which computing instructions in the processor can be carried out. However, this clock rate cannot be increased much further and in recent computational architectures (such as GPU, Intel Phi) additional computational power is now provided through having (of the order of) hundreds of computational cores in the same unit. This opens up potential for new order of magnitude performance improvements but requires additional specialist training in parallel programming and computational methods to be able to tap into and exploit this opportunity. Computational advances are enabled by new hardware, and innovations in algorithms, numerical methods and simulation techniques, and application of best practice in scientific computational modelling. The most effective progress and highest impact can be obtained by combining, linking and simultaneously exploiting step changes in hardware, software, methods and skills. However, good computational science training is scarce, especially at post-graduate level. The Centre for Doctoral Training in Next Generation Computational Modelling will develop 55+ graduate students to address this skills gap. Trained as future leaders in Computational Modelling, they will form the core of a community of computational modellers crossing disciplinary boundaries, constantly working to transfer the latest computational advances to related fields. By tackling cutting-edge research from fields such as Computational Engineering, Advanced Materials, Autonomous Systems and Health, whilst communicating their advances and working together with a world-leading group of academic and industrial computational modellers, the students will be perfectly equipped to drive advanced computing over the coming decades.

Simulation models are used in many organisations for planning and better managing organisational systems e.g. manufacturing plant or service operations. A key part of the process of developing and using a simulation model is to experiment with the model. In order to obtain accurate measures of a model's performance care must be taken to obtain sufficient good data from the model. Particular issues are removing initialisation bias, running the model for long enough and performing sufficient replications (runs with different streams of random numbers). Decisions regarding these issues require statistical skills which many simulation modellers do not possess. As a result, many simulation models may be used poorly and incorrect conclusions reached. This research aims to develop an 'analyser' that will automatically analyse the output from a simulation model and advise the simulation modeller on an appropriate warm-up period, run-length and number of replications. In the first stage of the research existing methods for analysing simulation output will be tested to identify candidate methods for inclusion in the analyser. Candidate methods will then be adapted where necessary to make them suitable for automation. In the final stage of the research a prototype analyser will be developed and tested. The methods and analyser will be tested on example data, using real simulation models and with simulation users.

The ambition of this project is to use a mix of factory activity data to optimise industrial operations, and to identify opportunities and deliver improvements in efficiency, productivity and sustainability. The rapid advance of digital sensing technologies, is making the real time recording of activities in a manufacturing environment both practical and affordable. However, the availability of diverse, real time data about movement and activity does not automatically help engineers manage the complex, dynamic environments typical of modern industrial operations. To do this they need tools that support their interpretation of constantly changing data in ways that enhance productivity and sustainability. In other words, the research challenge posed by digital manufacturing is not the capture of data, but rather the lack of computational methods to analyse large flows of diverse (i.e. multimodal) sensor data and recognise the patterns that allow engineers to assess the current state of the shop floor, understand the impact of past events and predict the consequences of incidents on a range measures. Motivated by this need, the following proposal details a program of work to investigate if the forms of probabilistic networks that have been employed to generate computational models from location tracking data in other contexts (e.g. vehicles movements in traffic models and the daily routines of individuals in domestic environments) can be extended to work with multiple forms of industrial activity data recorded on a factory floor. Such a model would allow diverse signals of manufacturing activity (e.g. material transport, staff movement, vibration, electrical current and air quality etc.) to be used to infer the behaviour of an industrial workplace and generate quantitative measures that support decisions which impact on a sites' production and sustainability performance.