Showing 1 - 2 of 2 UKAEA Paper Results
2023
UKAEA-CCFE-PR(23)123
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 characterise the historical information and encode the required temporal dependencies. However, most existing sequential models operate within finite-dimensional Euclidean spa…
Preprint2019
UKAEA-CCFE-PR(20)123
Vignesh Gopakumar D. Samaddar
We propose a method for data-driven modelling of the temporal evolution of the plasma and neutral characteristics at the edge of a Tokamak using neural networks. Our method proposes a novel fully convolutional network that is capable of predicting the time evolution of the physics parameters along the two-dimensional representation of the poloid…
Preprint Published