Vignesh Gopakumar

16 papers A* 1A 1Journal 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Learning Physical Operators using Neural Operators. CoRR Vignesh Gopakumar, Ander Gray, Daniel Giles, Lorenzo Zanisi, Matt J. Kusner, Timo Betcke, Stanislas Pamela, Marc Peter Deisenroth
2025	A*	`conf`	Calibrated Physics-Informed Uncertainty Quantification. ICML Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Daniel Giles, Matt J. Kusner, Stanislas Pamela, Marc Peter Deisenroth
2025	J	`jnl`	Calibrated Physics-Informed Uncertainty Quantification. CoRR Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2025	A	`conf`	Guaranteed Prediction Sets for Functional Surrogate Models. UAI Ander Gray, Vignesh Gopakumar, Sylvain Rousseau, Sébastien Destercke
2025	J	`jnl`	Guaranteed confidence-band enclosures for PDE surrogates. CoRR Ander Gray, Vignesh Gopakumar, Sylvain Rousseau, Sébastien Destercke
2025	J	`jnl`	Leveraging AI modelling for FDS with Simvue: monitor and optimise for more sustainable simulations. CoRR James Panayis, Matt Field, Vignesh Gopakumar, Andrew Lahiff, Kristian Zarebski, Aby Abraham, Jonathan L. Hodges
2025	J	`jnl`	Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators. Comput. Phys. Commun. Stanislas Pamela, Naomi Carey, Johannes Brandstetter, Rob J. Akers, Lorenzo Zanisi, James Buchanan, Vignesh Gopakumar, Matthias Hoelzl, Guido Huijsmans, Kamran Pentland, James Tickner, G. Antonucci
2024	J	`jnl`	Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction. CoRR Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2024	J	`jnl`	Valid Error Bars for Neural Weather Models using Conformal Prediction. CoRR Vignesh Gopakumar, Joel Oskarrson, Ander Gray, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth
2023	J	`jnl`	Fast regression of the tritium breeding ratio in fusion reactors. Mach. Learn. Sci. Technol. Petr Mánek, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann
2023	J	`jnl`	Fourier-RNNs for Modelling Noisy Physics Data. CoRR Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi
2023	J	`jnl`	Plasma Surrogate Modelling using Fourier Neural Operators. CoRR Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt J. Kusner, Marc Peter Deisenroth, Anima Anandkumar, JOREK Team, MAST Team
2023	J	`jnl`	Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation. CoRR Timothy Nunn, Vignesh Gopakumar, Sebastien Kahn
2022	J	`jnl`	Loss Landscape Engineering via Data Regulation on PINNs. CoRR Vignesh Gopakumar, Stanislas Pamela, Debasmita Samaddar
2021	J	`jnl`	Fast Regression of the Tritium Breeding Ratio in Fusion Reactors. CoRR Petr Mánek, Graham W. Van Goffrier, Vignesh Gopakumar, Nikolaos Nikolaou, Jonathan Shimwell, Ingo P. Waldmann
2020	J	`jnl`	Image mapping the temporal evolution of edge characteristics in tokamaks using neural networks. Mach. Learn. Sci. Technol. Vignesh Gopakumar, D. Samaddar