关注
Vignesh Gopakumar
Vignesh Gopakumar
UK Atomic Energy Authority, University College London
在 ukaea.uk 的电子邮件经过验证
标题
引用次数
引用次数
年份
Plasma surrogate modelling using Fourier neural operators
V Gopakumar, S Pamela, L Zanisi, Z Li, A Gray, D Brennand, N Bhatia, ...
Nuclear Fusion 64 (5), 056025, 2024
23*2024
Loss landscape engineering via data regulation on PINNs
V Gopakumar, S Pamela, D Samaddar
Machine Learning with Applications 12, 100464, 2023
152023
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
62020
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
52024
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
32024
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
32022
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
32021
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
22024
Fourier-RNNs for modelling noisy physics data
V Gopakumar, S Pamela, L Zanisi
arXiv preprint arXiv:2302.06534, 2023
22023
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
12023
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
12022
Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation
T Nunn, UKAE Authority, V Gopakumar, S Kahn
12021
Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction
V Gopakumar, A Gray, J Oskarsson, L Zanisi, S Pamela, D Giles, ...
arXiv preprint arXiv:2408.09881, 2024
2024
Valid Error Bars for Neural Weather Models using Conformal Prediction
V Gopakumar, J Oskarsson, A Gray, L Zanisi, S Pamela, D Giles, ...
arXiv preprint arXiv:2406.14483, 2024
2024
Neural-Parareal: Dynamically Training Neural Operators as Coarse Solvers for Time-Parallelisation of Fusion MHD Simulations
SJP Pamela, N Carey, J Brandstetter, R Akers, L Zanisi, J Buchanan, ...
arXiv preprint arXiv:2405.01355, 2024
2024
Monitoring and tracking machine learning models for fire sprinkler design in real-time using Simvue
VG Aby Abraham, James Panayis, Matthew Field, Kristian Zarebski, Andrew Lahiff
RSE Conference 8, 2024
2024
Overview of the EUROfusion Tokamak Exploitation programme in support of ITER and DEMO
EH Joffrin, M Wischmeier, M Baruzzo, A Hakola, A Kappatou, D Keeling, ...
Nuclear Fusion, 2023
2023
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
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