39 citations · 91 across the 19 of their papers we have counts for
21 papers
Learnable Splines for Unstructured Neural Operators
Vignesh Gopakumar, Zhuoyuan Wang, Ander Gray +12
Neural operators approximate solution operators of partial differential equations at a fraction of the cost of numerical solvers. Efficient grid-based architectures such as the Fou…
PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems
Yubo Cao, Xi Deng, Mengqi Xia +3
Particle transport under multiple scattering is central to radiative transfer and plasma physics, yet high-fidelity Monte Carlo (MC) simulations must trace prohibitively many parti…
Data-Efficient Neural Operator Training via Physics-Based Active Learning
Alicja Polanska, Lorenzo Zanisi, Vignesh Gopakumar +1
Solving partial differential equations with neural operators significantly reduces computational costs but remains bottlenecked by high training data requirements. Active learning…
Learning Physical Operators using Neural Operators
Vignesh Gopakumar, Ander Gray, Dan Giles +5
Neural operators have emerged as promising surrogate models for solving partial differential equations (PDEs), but struggle to generalise beyond training distributions and are ofte…
Leveraging AI modelling for FDS with Simvue: monitor and optimise for more sustainable simulations
James Panayis, Matt Field, Vignesh Gopakumar +4
There is high demand on fire simulations, in both scale and quantity. We present a multi-pronged approach to improving the time and energy required to meet these demands. We show t…
Bayesian optimisation of poloidal field coil positions in tokamaks
Timothy Nunn, Kamran Pentland, Vignesh Gopakumar +1
The tokamak is a world-leading concept for producing sustainable energy via magnetically-confined nuclear fusion. Identifying where to position the magnets within a tokamak, specif…
Neural operator surrogate models of plasma edge simulations: feasibility and data efficiency
N. Carey, L. Zanisi, S. Pamela +7
The inclusion of high-fidelity simulations of SOL turbulence and transient MHD events such as ELMs in highly iterative applications remains computationally prohibitive, limiting th…
Calibrated Physics-Informed Uncertainty Quantification
Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi +5
Simulating complex physical systems is crucial for understanding and predicting phenomena across diverse fields, such as fluid dynamics and heat transfer, as well as plasma physics…
Guaranteed prediction sets for functional surrogate models
Ander Gray, Vignesh Gopakumar, Sylvain Rousseau +1
We propose a method for obtaining statistically guaranteed prediction sets for functional machine learning methods: surrogate models which map between function spaces, motivated by…
Uncertainty Quantification of Surrogate Models using Conformal Prediction
Vignesh Gopakumar, Ander Gray, Joel Oskarsson +5
Data-driven surrogate models offer quick approximations to complex numerical and experimental systems but typically lack uncertainty quantification, limiting their reliability in s…
Valid Error Bars for Neural Weather Models using Conformal Prediction
Vignesh Gopakumar, Joel Oskarrson, Ander Gray +5
Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do…
Neural-Parareal: Dynamically Training Neural Operators as Coarse Solvers for Time-Parallelisation of Fusion MHD Simulations
S. J. P. Pamela, N. Carey, J. Brandstetter +10
The fusion research facility ITER is currently being assembled to demonstrate that fusion can be used for industrial energy production, while several other programmes across the wo…
Data efficiency and long term prediction capabilities for neural operator surrogate models of core and edge plasma codes
N. Carey, L. Zanisi, S. Pamela +4
Simulation-based plasma scenario development, optimization and control are crucial elements towards the successful deployment of next-generation experimental tokamaks and Fusion po…
Plasma Surrogate Modelling using Fourier Neural Operators
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi +10
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rap…
Efficient training sets for surrogate models of tokamak turbulence with Active Deep Ensembles
L. Zanisi, A. Ho, T. Madula +7
Model-based plasma scenario development lies at the heart of the design and operation of future fusion powerplants. Including turbulent transport in integrated models is essential…
Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation
Timothy Nunn, Vignesh Gopakumar, Sebastien Kahn
Nuclear fusion using magnetic confinement holds promise as a viable method for sustainable energy. However, most fusion devices have been experimental and as we move towards energy…
Multi-objective Bayesian optimization for design of Pareto-optimal current drive profiles in STEP
Theodore Brown, Stephen Marsden, Vignesh Gopakumar +3
The safety factor profile is a key property in determining the stability of tokamak plasmas. To design the safety factor profile in the United Kingdom's proposed Spherical Tokamak…
Fourier Neural Operator for Plasma Modelling
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi +3
Predicting plasma evolution within a Tokamak is crucial to building a sustainable fusion reactor. Whether in the simulation space or within the experimental domain, the capability…
Fourier-RNNs for Modelling Noisy Physics Data
Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi
Classical sequential models employed in time-series prediction rely on learning the mappings from the past to the future instances by way of a hidden state. The Hidden states chara…
Loss Landscape Engineering via Data Regulation on PINNs
Vignesh Gopakumar, Stanislas Pamela, Debasmita Samaddar
Physics-Informed Neural Networks have shown unique utility in parameterising the solution of a well-defined partial differential equation using automatic differentiation and residu…
Fast Regression of the Tritium Breeding Ratio in Fusion Reactors
Petr Mánek, Graham Van Goffrier, Vignesh Gopakumar +3
The tritium breeding ratio (TBR) is an essential quantity for the design of modern and next-generation D-T fueled nuclear fusion reactors. Representing the ratio between tritium fu…