| Loss landscape engineering via data regulation on PINNs V Gopakumar, S Pamela, D Samaddar Machine Learning with Applications 12, 100464, 2023 | 9 | 2023 |
| Image mapping the temporal evolution of edge characteristics in tokamaks using neural networks V Gopakumar, D Samaddar Machine Learning: Science and Technology 1 (1), 015006, 2020 | 6 | 2020 |
| Fourier neural operator for plasma modelling V Gopakumar, S Pamela, L Zanisi, Z Li, A Anandkumar, M Team arXiv preprint arXiv:2302.06542, 2023 | 3 | 2023 |
| Active learning pipeline for surrogate models of gyrokinetic turbulence J Burr, T Madula, L Zanisi, A Ho, J Citrin, V Gopakumar, S Pamela, ... APS Division of Plasma Physics Meeting Abstracts 2022, BP11. 002, 2022 | 3 | 2022 |
| 14 MeV neutron irradiation experiments-gamma spectroscopy analysis and validation automation T Stainer, MR Gilbert, LW Packer, S Lilley, V Gopakumar, C Wilson EPJ Web of Conferences 247, 09010, 2021 | 3 | 2021 |
| Plasma Surrogate Modelling using Fourier Neural Operators V Gopakumar, S Pamela, L Zanisi, Z Li, A Gray, D Brennand, N Bhatia, ... arXiv preprint arXiv:2311.05967, 2023 | 2 | 2023 |
| Multi-Objective Bayesian Optimization for Design of Pareto-Optimal Current Drive Profiles in STEP T Brown, S Marsden, V Gopakumar, A Terenin, H Ge, F Casson IEEE Transactions on Plasma Science, 2024 | 1 | 2024 |
| Data efficiency and long term prediction capabilities for neural operator surrogate models of core and edge plasma codes N Carey, L Zanisi, S Pamela, V Gopakumar, J Omotani, J Buchanan, ... arXiv preprint arXiv:2402.08561, 2024 | 1 | 2024 |
| Efficient training sets for surrogate models of tokamak turbulence with Active Deep Ensembles L Zanisi, A Ho, J Barr, T Madula, J Citrin, S Pamela, J Buchanan, ... Nuclear Fusion 64 (3), 036022, 2024 | 1 | 2024 |
| Fast regression of the tritium breeding ratio in fusion reactors P Mánek, G Van Goffrier, V Gopakumar, N Nikolaou, J Shimwell, ... Machine Learning: Science and Technology 4 (1), 015008, 2023 | 1 | 2023 |
| Fourier-RNNs for Modelling Noisy Physics Data V Gopakumar, S Pamela, L Zanisi arXiv preprint arXiv:2302.06534, 2023 | 1 | 2023 |
| Towards real-time fusion reactor design using the Omniverse L Margetts, R Akers, A Ghosh, V Gopakumar, P Hadorn, M Hummel, ... Nvidia GPU Technology Conference, 2022 | 1 | 2022 |
| Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation T Nunn, UKAE Authority, V Gopakumar, S Kahn | 1 | 2021 |
| Spatio-temporal forecasting of plasma turbulence using deep learning R Gaur, V Gopakumar, N Barbour, B Jang, N Mandell, I Abel, W Dorland, ... APS Division of Plasma Physics Meeting Abstracts 2023, JO09. 015, 2023 | | 2023 |
| Active and continual learning of fusion plasma turbulence surrogate models for digital twinning of a tokamak device J Barr, T Madula, L Zanisi, V Gopakumar, A Ho, J Citrin, JET Contributors ReALML@ICML, https://realworldml.github.io/, 2022 | | 2022 |
| Development of fusion reactor digital twins in the Metaverse L Margetts, R Akers, A Ghosh, V Gopakumar, P Hadorn, M Hummel, ... IET Nuclear Engineering for Safety, Control and Security, 2022 | | 2022 |
| Informed Sampling of the Plasma Hyperspace for Digital Twinning M Bakrania, V Gopkakumar IAEA Technical Meeting on Fusion Data Processing, Validation, Analysis 4, 2021 | | 2021 |
| Evaluating Imprecise Probabilities in Fusion Plasma Surrogates Using Conformal Prediction A Gray, V Gopakumar, W Hornsby, J Buchanan, S Pamela | | |
| Publication: European Physical Journal Web of Conferences Pub Date: April 2021 T Stainer, MR Gilbert, LW Packer, S Lilley, V Gopakumar, C Wilson | | |
Stanislas PamelaCCFE - UKAEA - UKVerified email at ukaea.uk
