<![CDATA[Conventional methods of circuit simulation such as full-wave electromagnetic ï¬eldsolvers can be very slow. Machine learning is an emerging technology in modelling, simulation, optimization, and design that present attractive alternatives to the conventional methodologies because they can be trained with a small amount of data, and then used to perform fast circuit predictions within the same design space. In this paper, we present applications of machine learning techniques for the modelling of transmission lines from their impulse reponses. The standard multilayer perceptron (MLP) neural network and the gaussian process (GP) regression techniques are demonstrated, andboth models are successfully implemented to model the impulse responses of transmission lines with great accuracies. We show that the GP outperforms the MLP in terms of prediction accuracies and that the GP is more data efï¬cient than the MLP. This is beneï¬cial considering that each training sample is expensive, making the GP a good candidate for the task, compared to the more popular MLP.]]>
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