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UKAEA-CCFE-PR(23)1232023
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…
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UKAEA-CCFE-PR(25)2782022
Physics-Informed Neural Networks have shown unique utility in parameterising the solution of a well-defined partial differential equation using automatic differentiation and residual losses. Though they provide theoretical guarantees of convergence, in practice the required training regimes tend to be exacting and demanding. Through the course o…
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UKAEA-CCFE-PR(20)1232019
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…
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