Publications

Highlighted

  • Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction
    • Authors: Gopakumar, Vignesh; Gray, Ander; Oskarsson, Joel; Zanisi, Lorenzo; Pamela, Stanislas; Giles, Daniel; Kusner, Matt; Deisenroth, Marc
    • Published in: arXiv preprint arXiv:2408.09881, 2024
    • TLDR: [Brief summary of the paper]
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  • Valid Error Bars for Neural Weather Models using Conformal Prediction
    • Authors: Gopakumar, Vignesh; Oskarsson, Joel; Gray, Ander; Zanisi, Lorenzo; Pamela, Stanislas; Giles, Daniel; Kusner, Matt; Deisenroth, Marc
    • Published in: arXiv preprint arXiv:2406.14483, 2024
    • TLDR: [Brief summary of the paper]
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  • Plasma surrogate modelling using Fourier neural operators
    • Authors: Gopakumar, Vignesh; Pamela, Stanislas; Zanisi, Lorenzo; Li, Zongyi; Gray, Ander; Brennand, Daniel; Bhatia, Nitesh; Stathopoulos, Gregory; Kusner, Matt; Deisenroth, Marc
    • Published in: Nuclear Fusion, Volume 64, Number 5, 2024
    • TLDR: [Brief summary of the paper]
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  • Loss landscape engineering via data regulation on PINNs
    • Authors: Gopakumar, Vignesh; Pamela, Stanislas; Samaddar, Debasmita
    • Published in: Machine Learning with Applications, Volume 12, 2023
    • TLDR: [Brief summary of the paper]
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  • Fourier-RNNs for modelling noisy physics data
    • Authors: Gopakumar, Vignesh; Pamela, Stanislas; Zanisi, Lorenzo
    • Published in: arXiv preprint arXiv:2302.06534, 2023
    • TLDR: [Brief summary of the paper]
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  • Image mapping the temporal evolution of edge characteristics in tokamaks using neural networks
    • Authors: Gopakumar, Vignesh; Samaddar, Debasmita
    • Published in: Machine Learning: Science and Technology, Volume 1, Number 1, 2020
    • TLDR: [Brief summary of the paper]
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Other

  • Neural-Parareal: Self-Improving Acceleration of Fusion MHD Simulations Using Time-Parallelisation and Neural Operators
    • Authors: Pamela, SJP; Carey, N; Brandstetter, J; Akers, R; Zanisi, L; Buchanan, J; Gopakumar, V; Hoelzl, M; Huijsmans, G; Pentland, K
    • Published in: Computer Physics Communications, 2024
    • TLDR: [Brief summary of the paper]
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  • Data efficiency and long term prediction capabilities for neural operator surrogate models of core and edge plasma codes
    • Authors: Carey, N; Zanisi, L; Pamela, S; Gopakumar, V; Omotani, J; Buchanan, J; Brandstetter, J
    • Published in: arXiv preprint arXiv:2402.08561, 2024
    • TLDR: [Brief summary of the paper]
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  • Efficient training sets for surrogate models of tokamak turbulence with active deep ensembles
    • Authors: Zanisi, L; Ho, A; Barr, J; Madula, T; Citrin, J; Pamela, S; Buchanan, J; Casson, FJ; Gopakumar, V; Contributors, JET
    • Published in: Nuclear Fusion, Volume 64, Number 3, 2024
    • TLDR: [Brief summary of the paper]
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  • Multi-Objective Bayesian Optimization for Design of Pareto-Optimal Current Drive Profiles in STEP
    • Authors: Brown, Theodore; Marsden, Stephen; Gopakumar, Vignesh; Terenin, Alexander; Ge, Hong; Casson, Francis
    • Published in: IEEE Transactions on Plasma Science, 2024
    • TLDR: [Brief summary of the paper]
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  • Overview of the EUROfusion Tokamak Exploitation programme in support of ITER and DEMO
    • Authors: Joffrin, Emmanuel Henri; Wischmeier, Marco; Baruzzo, Matteo; Hakola, Antti; Kappatou, Athina; Keeling, David; Labit, Benoit; Tsitrone, Emmanuelle; Vianello, Nicola
    • Published in: Nuclear Fusion, 2023
    • TLDR: [Brief summary of the paper]
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  • Spatio-temporal forecasting of plasma turbulence using deep learning
    • Authors: Gaur, Rahul; Gopakumar, Vignesh; Barbour, Nathaniel; Jang, Byoungchan; Mandell, Noah; Abel, Ian; Dorland, William; Kolemen, Egemen
    • Presented at: APS Division of Plasma Physics Meeting Abstracts, 2023
    • TLDR: [Brief summary of the paper]
    • Presentation
  • Fast regression of the tritium breeding ratio in fusion reactors
    • Authors: Mánek, Petr; Van Goffrier, Graham; Gopakumar, Vignesh; Nikolaou, Nikolaos; Shimwell, Jonathan; Waldmann, Ingo
    • Published in: Machine Learning: Science and Technology, Volume 4, Number 1, 2023
    • TLDR: [Brief summary of the paper]
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  • Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation
    • Authors: Nunn, Timothy; Authority, UK Atomic Energy; Gopakumar, Vignesh; Kahn, Sebastien
    • Published in: Bayesian Optimisation Workshop @ NeurIPS 2022
    • TLDR: [Brief summary of the paper]
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  • Development of fusion reactor digital twins in the Metaverse
    • Authors: Margetts, Lee; Akers, Rob; Ghosh, Abhijeet; Gopakumar, Vignesh; Hadorn, Patrik; Hummel, Mathias; Messmer, Peter; Omer, Muhammad; Ozturk, Ekin; Pamela, Stanislav
    • Published in: IET Nuclear Engineering for Safety, Control and Security, 2022
    • TLDR: [Brief summary of the paper]
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  • Active and continual learning of fusion plasma turbulence surrogate models for digital twinning of a tokamak device
    • Authors: Barr, Jackson; Madula, Thandikire; Zanisi, Lorenzo; Gopakumar, Vignesh; Ho, Aaron; Citrin, Jonathan; Contributors, and JET
    • Presented at: ReALML@ICML, 2022
    • TLDR: [Brief summary of the paper]
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  • 14 MeV neutron irradiation experiments-gamma spectroscopy analysis and validation automation
    • Authors: Stainer, Thomas; Gilbert, Mark R; Packer, Lee W; Lilley, Steven; Gopakumar, Vignesh; Wilson, Chris
    • Published in: EPJ Web of Conferences, Volume 247, 2021
    • TLDR: [Brief summary of the paper]
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  • Informed Sampling of the Plasma Hyperspace for Digital Twinning
    • Authors: Bakrania, Mayur; Gopkakumar, Vignesh
    • Presented at: IAEA Technical Meeting on Fusion Data Processing, Validation, Analysis, 2021
    • TLDR: [Brief summary of the paper]
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