Active Physics & Astronomy Engineering

EPSRC Centre for Doctoral Training in Collaborative Computational Modelling at the Interface

In plain English

AI plain-English summary

Computational models that predict the weather, design earthquake-proof buildings, and simulate better batteries have hit a wall—they cannot handle problems like high-dimensional control or multiscale fluid flow, and for tasks like natural language processing, no physical model exists at all. This Centre for Doctoral Training trains researchers to bridge three separate worlds: physics-based modelling, data-driven approaches (like deep learning), and research software engineering. Traditionally treated as a support service, software engineering is now recognised as an equal academic pillar—hardware constraints and software design actively shape what computational methods can achieve. The UK’s Independent Review of the Future of Compute recently underscored the need to pair infrastructure investment with skills programmes. If successful, this programme will produce graduates who can move fluently across modelling and software engineering, creating solutions for digital twins in personalised medicine and for simulating climate change impacts. The work is not about a single immediate application; it builds the human talent needed to tackle the hardest computational problems that neither pure physics nor pure data approaches can solve alone.

View original technical description
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.

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Researchers

Colin Cotter (Co-Investigator)Dante Kalise Balza (Co-Investigator)Hao Ni (Co-Investigator)Marta Betcke (Co-Investigator)Ruth Misener (Co-Investigator)Serge Guillas (Co-Investigator)Vahid Shahrezaei (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

EPSRC Centre for Doctoral Training in Next Generation Computational Modelling
EPSRC Centre for Doctoral Training in Theory and Modelling in Chemical Sciences.
EPSRC Centre for Doctoral Training in Distributed Algorithms: the what, how and where of next-generation data science
EPSRC Centre for Doctoral Training in Mathematics for Real-World Systems II
EPSRC Centre for Doctoral Training in Mathematics of Random Systems: Analysis, Modelling and Simulation

Original classification

Training Grant

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