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| Reference Number | EP/Y034767/1 | |
| Title | EPSRC Centre for Doctoral Training in Collaborative Computational Modelling at the Interface | |
| Status | Started | |
| Energy Categories | Other Cross-Cutting Technologies or Research 5%; Not Energy Related 95%; |
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| Research Types | Training 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 50%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Dr T Betcke No email address given Mathematics University College London |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 01 July 2024 | |
| End Date | 30 September 2033 | |
| Duration | 111 months | |
| Total Grant Value | £8,795,896 | |
| Industrial Sectors | Information Technologies | |
| Region | London | |
| Programme | EPSRC Training Grants | |
| Investigators | Principal Investigator | Dr T Betcke , Mathematics, University College London (99.993%) |
| Other Investigator | Dr MM Betcke , Computer Science, University College London (0.001%) Dr C Cotter , Mathematics, Imperial College London (0.001%) Professor SE Guillas , Statistical Science, University College London (0.001%) Dr DF Kalise Balza , Mathematics, Imperial College London (0.001%) Dr R Misener , Computing, Imperial College London (0.001%) Professor H Ni , Mathematics, University College London (0.001%) Dr V Shahrezaei , Mathematics, Imperial College London (0.001%) |
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| Industrial Collaborator | Project Contact , Schlumberger Cambridge Research Ltd (0.000%) Project Contact , EURATOM/CCFE (0.000%) Project Contact , McLaren Racing Ltd (0.000%) Project Contact , Oak Ridge National Laboratory, USA (0.000%) Project Contact , Gurobi Optimization (0.000%) Project Contact , Dummy Organisation (0.000%) Project Contact , DELL Technologies (0.000%) Project Contact , Amazon Web Services EMEA SARL (0.000%) Project Contact , Isaac Newton Institute (0.000%) Project Contact , JP Morgan Chase (0.000%) Project Contact , Devito Codes Ltd (0.000%) Project Contact , Enthought Ltd (0.000%) Project Contact , GAMS Software GmbH (0.000%) Project Contact , Graphcore (0.000%) Project Contact , Netherlands eScience Center (0.000%) Project Contact , Rafinex S. a r.l. (0.000%) Project Contact , Rapiscan (Global) (0.000%) Project Contact , SURF (0.000%) Project Contact , The Francis Crick Institute (0.000%) |
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| Web Site | ||
| Objectives | ||
| Abstract | Since the advent of numerical weather prediction in the early twentieth century, physics driven computational modelling has gone from strength to strength, underpinning much of the modern world, from the design of new bridges and buildings that can withstand earthquakes, to the aerodynamic optimisation of airplanes and the simulation of materials for batteries underpinning the electric car revolution. But physics based models alone have limits in what they can do. From high dimensional control problems to multiscale fluid flow, there are many important systems where conventional discretise-and-solve approaches remain permanently out reach. In other important systems, we have no physical models at all (in natural language processing and many other areas). In these data based approaches we have seen tremendous advances over the last decade, exemplified by the deep learning revolution. There is a now a growing consensus that computational models of tomorrow will consist of combinations of physics and data driven approaches and should not be viewed separately from each other.There is one more missing ingredient, attaining increasing recognition by research labs across the world, namely research software engineering. Traditionally seen as a professional service to support the implementation of computational models, research software engineering now emerges as an equal academic pillar to computational mathematics and data driven approaches. Software design, and hardware limitations, inform and shape the design of computational methods. Researchers need to take a holistic view across computational modelling and software engineering to create truly innovative solutions to the truly challenging problems from digital twins in personal medicine to simulating and mitigating the effects of climate change.This CDT has been designed around the need to train graduates across the interfaces of physics and data driven computational modelling and research software engineering. Our trainees will be able to engage with challenging problems not only from a modelling perspective but also from a software perspective, moving fluently across modelling and research software engineering.The subsequent urgent need for training in research software engineering at the highest level is also increasingly recognised by research centres across the world. We have partnered with a number of institutions in this proposal who follow this vision. In the UK this has been recently exemplified by the Independent Review of the Future of Compute, which recognised the importance of pairing infrastructure investments with skills programmes, and the importance of creating, attracting and retaining world class compute talent. Paired with an innovative training programme around interface working groups and software projects, our graduates will participate in and shape world leading research across the mathematics of data enhanced computational modelling, the design of corresponding computational algorithms, scientific research software engineering, and domain specific applications | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 24/07/24 | |
