Computational Mathematics
Experimental science revolves around comparing data to models, optimising experiment and developing approaches to preserve samples or extract information in more effective ways. Maths and AI are interlinked and sit at the heart of the facilities operations, experiments and experimental analysis.
For example,
- Iterative solvers and inverse problems are used to reconstruct 3D images from data in computed tomography
- Electron beam and motors are controlled and moved using algorithms from control theory
- Data is fitted using mathematical optimisation
- Experiments and parameters are optimised using bayesian optimisation
- AI model training is an optimisation problem and the use of AI surrogate models is typically coupled to bayesian optimisation to tune outputs
- Atomistic simulations and engineering models use advanced linear algebra libraries.
The Computational Maths theme acts as a link to transfer knowledge of methods across facilities, and across projects within a facility.
Our scientific facilities are producing data with more velocity and fidelity than ever before, and this rate is increasing.
In order to analyse this data, increasing the compute power is not enough, especially against a background of increasing awareness about the environmental impact of computational resources. New mathematics and new algorithms are required to analyse experimental data with sufficient accuracy, in an acceptable time.
Computational Maths focuses on innovative research into the underlying fundamental mathematics which help to drive facility operations and science with specific expertise in:
- Data fitting
- Inverse problems
- Nonlinear optimization
- Sparse eigenvalue problems
- Sparse linear systems
- Tensor methods
FAQs
What is the Ada Lovelace Centre?
The Ada Lovelace Centre is a collaborative hub that helps UK science facilities use computing to solve complex research challenges. It brings together experts in data, software, and artificial intelligence to accelerate scientific progress.
What do we offer?
The Ada Lovelace Centre will fund 50% of the cost of a studentship (fees and stipend) with a partner University.
Who can apply?
Any academic belonging to a UK university with the authorisation to supervise PhD students may apply as the university supervisor.
Who can work with the Ada Lovelace Centre?
Researchers, technical professionals, and organisations can engage with the Ada Lovelace Centre by proposing collaborative projects, joining challenge-led initiatives, or exploring shared computing solutions across STFC’s facilities.
