39 citations · 91 across the 19 of their papers we have counts for
6 papers · 1 filter
Plasma Surrogate Modelling using Fourier Neural Operators
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi +10
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rap…
Efficient training sets for surrogate models of tokamak turbulence with Active Deep Ensembles
L. Zanisi, A. Ho, T. Madula +7
Model-based plasma scenario development lies at the heart of the design and operation of future fusion powerplants. Including turbulent transport in integrated models is essential…
Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation
Timothy Nunn, Vignesh Gopakumar, Sebastien Kahn
Nuclear fusion using magnetic confinement holds promise as a viable method for sustainable energy. However, most fusion devices have been experimental and as we move towards energy…
Multi-objective Bayesian optimization for design of Pareto-optimal current drive profiles in STEP
Theodore Brown, Stephen Marsden, Vignesh Gopakumar +3
The safety factor profile is a key property in determining the stability of tokamak plasmas. To design the safety factor profile in the United Kingdom's proposed Spherical Tokamak…
Fourier Neural Operator for Plasma Modelling
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi +3
Predicting plasma evolution within a Tokamak is crucial to building a sustainable fusion reactor. Whether in the simulation space or within the experimental domain, the capability…
Fourier-RNNs for Modelling Noisy Physics Data
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi
Classical sequential models employed in time-series prediction rely on learning the mappings from the past to the future instances by way of a hidden state. The Hidden states chara…