Plasma Surrogate Modelling using Fourier Neural Operators

no code implementations • 10 Nov 2023 • Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt Kusner, Marc Peter Deisenroth, Anima Anandkumar, JOREK Team, MAST Team

Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion.

Super-Resolution

Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation

no code implementations • 2 Oct 2023 • Timothy Nunn, Vignesh Gopakumar, Sebastien Kahn

The optimisation helps to identify design parameters that would minimise the costs incurred while maximising the plasma stability by way of minimising magnetic ripples.

Bayesian Optimisation

Fourier-RNNs for Modelling Noisy Physics Data

no code implementations • 13 Feb 2023 • Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi

The Fourier-RNN allows for learning the mappings from the input to the output as well as to the hidden state within the Fourier space associated with the temporal data.

Operator learning Time Series +1

Loss Landscape Engineering via Data Regulation on PINNs

no code implementations • 16 May 2022 • Vignesh Gopakumar, Stanislas Pamela, Debasmita Samaddar

The data regulates and morphs the topology of the loss landscape associated with the PINN to make it easily traversable for the minimiser.

Fast Regression of the Tritium Breeding Ratio in Fusion Reactors

no code implementations • 8 Apr 2021 • Petr Mánek, Graham Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo Waldmann

The tritium breeding ratio (TBR) is an essential quantity for the design of modern and next-generation D-T fueled nuclear fusion reactors.

regression