Group
Computational and Industrial Mathematics
Unit(s) of assessment: General Engineering
School: School of Science and Technology
Overview
Design and construction decisions are increasingly made by means of virtual prototyping and as part of Computer Aided Engineering. Efficient simulation tools in all areas of engineering are prevalent and sought-after. The increasing availability of computational resources since the mid-twentieth century has nurtured the growth of a simulation industry, serving the needs of end-users in the manufacturing sector. At the heart of these simulation methods are mathematical models, which have traditionally been physics-based models using differential equations. In more recent times, data-driven approaches using machine learning have also enjoyed widespread success.
The Computational and Industrial Mathematics research group develops bespoke mathematical and numerical models for problems arising in both engineering and the natural sciences, starting from the mathematical model at the core of the problem. Based within the Mathematical Sciences team and forming part of the Department of Physics and Mathematics, the group’s expertise spans both physics-based and data-driven models.
Collaboration
Academic collaborators
- Columbia University
- Heriot-Watt University
- Lawrence Berkeley National Laboratory
- Loughborough University
- Queen Mary University of London
- University of Brighton
- University of Leicester
- University of Nottingham
- University of Southampton
Industrial collaborators:
- Bowers and Wilkins Ltd
- CDH AG
- inuTech Gmbh
- Jaguar Land Rover Ltd
- Far UK Ltd
- PACYS Ltd
Members
Fixed-term contract staff:
- David Jenkins (Academic Associate)
- Tuan Bohoran (MSCA Research Fellow)
Publications
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Related projects
- Mid-High Frequency Modelling of Vehicle Noise and Vibration
- Stochastic transfer operator methods for modelling the vibroacoustic properties of newly emerging transport structures
- Transfer operator methods for modelling high-frequency wave fields - advancements through modern functional and numerical analysis
- Fully automated quantification of myocardial infarct size using artificial intelligence methods
- Designing weight and cost optimised automotive joints for novel lightweight materials using computer-aided engineering
- Revolutionising landslide-tsunami prediction with machine learning
- Initial-value and Scattering Problems for Detecting Delamination in Layered Waveguides