PRISM: Platform for Research In Simulation Methods

Lead Research Organisation: Imperial College London
Department Name: Dept of Aeronautics


Computational science is a multidisciplinary research endeavour spanning applied mathematics, computer science and engineering together with input from application areas across science, technology and medicine. Advanced simulation methods have the potential to revolutionise not only scientific research but also to transform the industrial economy, offering companies a competitive advantage in their products, better productivity, and an environment for creative exploration and innovation.

The huge range of topics that computational science encapsulates means that the field is vast and new methods are constantly being published. These methods relate not only to the core simulation techniques but also to problems which rely on simulation. These problems include quantifying uncertainty (i.e. asking for error bars), blending models with data to make better predictions, solving inverse problems (if the output is Y, what is the input X?), and optimising designs (e.g. finding a vehicle shape that is the most aerodynamic). Unfortunately, the process through which advanced new methods find their way into applications and industrial practice is very slow.

One of the reasons for this is that applying mathematical algorithms to complex simulation models is very intrusive; mostly they cannot treat the simulation code as a "black box". They often require rewriting of the software, which is very time consuming and expensive. In our research we address this problem by using automating the generation of computer code for simulation. The key idea is that the simulation algorithm is described in some abstract way (which looks as much like the underlying mathematics as possible, after thinking carefully about what the key aspects are), and specialised software tools are used to automatically build the computer code. When some aspect of the implementation needs to change (for example a new type of computer is being used) then these tools can be used to rebuild the code from the abstract description. This flexibility dramatically accelerates the application of advanced algorithms to real-world problems.

Consider the example of optimising the shape of a Formula 1 car to minimise its drag. The optimisation process is highly invasive: it must solve auxiliary problems to learn how to improve the design, and it be able to modify the shape used in the simulation at each iteration. Typically this invasiveness would require extensive modifications to the simulation software. But by storing a symbolic representation of the aerodynamic equations, all operations necessary for the optimisation can be generated in our system, without needing to rewrite or modify the aerodynamics code at all.

The research goal of our platform is to investigate and promote this methodology, and to produce publicly available, sustainable open-source software that ensures its uptake. The platform will allow us to make advances in our software approach that enables us to continue to secure industrial and government funding in the broad range of application areas we work in, including aerospace and automotive sectors, renewable energy, medicine and surgery, the environment, and manufacturing.

Planned Impact

Academic and industrial users of computational modelling software will benefit from this research since the outputs of the platform will give them access to robust performance-portable implementations of advanced simulation methods, including the composition of models with mathematical algorithms that can solve optimisation problems, quantify uncertainty, assimilate data, etc. This includes our own industrial collaborators from BAE Systems, Airbus, McLaren Racing, Rolls Royce, Arup Consulting, Meygen, EDF, AMEC, Shell, BP, Intel and NVIDIA. Computational modelling is becoming a greater part of the digital economy as a replacement for physical prototyping for many of these industries. Advanced computational modelling can be used to allow high-tech companies to obtain an edge over competitors, to improve productivity in their processes and products, and to provide a environment for creativity and innovation. We also collaborate with public sector research centres such as the UK Met Office, the National Oceanographic Centre in Southampton and Liverpool, and the British Antarctic Survey, for whom the improved modelling capability will enable them to better inform government policy on energy and the environment.

The platform team have an exceptional track record of delivering professionally engineered software tools which, in contrast to much academic software, are well designed, robustly tested, comprehensively documented and ready for translation into production use. This key distinguishing point is critical in guaranteeing that effective wider impact is actually achieved. The institutionalization of best practice in scientific software development also creates maintainable software with an effective and usable life far beyond that of the platform.

There is a continuing need for multidisciplinary researchers with skills in computer science, computational mathematics and numerical modelling. Our Platform grant will enable training in multiple disciplines to address this demand from both industry and academia. Our Platform directly targets the barriers to impact that prevent sophisticated modelling techniques from finding widespread application in industry and science. Further, software tools developed in the platform will support systematic, flexible mapping from the science and engineering "business requirements" of a numerical modelling project right down to the gates and wires of a computational simulation.

We will ensure the impact is maximized by holding regular events where we showcase our work and share ideas with industrial collaborators, and give our researchers the opportunity to network, including a stakeholder input workshop upon renewal. We will fund projects that bring our researchers into direct contact with industrial partners, building proof-of- concept products and interacting on benchmarks and challenges; thus disseminating our ideas and software and collecting industrial user needs. Further, we will aim to influence and keep up with the latest innovations from hardware vendors. The type of multidisciplinary experience that we provide to researchers on this project will make them experts in both numerical modelling and the necessary computer science and software engineering foundations, ensuring they become very employable within both academia and industry.


10 25 50