39 citations · 91 across the 19 of their papers we have counts for
5 papers · 1 filter
Leveraging AI modelling for FDS with Simvue: monitor and optimise for more sustainable simulations
James Panayis, Matt Field, Vignesh Gopakumar +4
There is high demand on fire simulations, in both scale and quantity. We present a multi-pronged approach to improving the time and energy required to meet these demands. We show t…
Bayesian optimisation of poloidal field coil positions in tokamaks
Timothy Nunn, Kamran Pentland, Vignesh Gopakumar +1
The tokamak is a world-leading concept for producing sustainable energy via magnetically-confined nuclear fusion. Identifying where to position the magnets within a tokamak, specif…
Neural operator surrogate models of plasma edge simulations: feasibility and data efficiency
N. Carey, L. Zanisi, S. Pamela +7
The inclusion of high-fidelity simulations of SOL turbulence and transient MHD events such as ELMs in highly iterative applications remains computationally prohibitive, limiting th…
Calibrated Physics-Informed Uncertainty Quantification
Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi +5
Simulating complex physical systems is crucial for understanding and predicting phenomena across diverse fields, such as fluid dynamics and heat transfer, as well as plasma physics…
Guaranteed prediction sets for functional surrogate models
Ander Gray, Vignesh Gopakumar, Sylvain Rousseau +1
We propose a method for obtaining statistically guaranteed prediction sets for functional machine learning methods: surrogate models which map between function spaces, motivated by…