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Visual Result Prediction in Electromagnetic Simulations Using Machine Learning

Karaosmanoglu, Bariscan
Ergül, Özgür Salih
In this letter, we present a novel approach based on using convolutional neural networks (CNNs) to visually predict solutions of electromagnetic problems. CNN models are constructed and trained such that images of surface currents obtained at the early stages of an iterative solution can be used to predict images of the final (converged) solution. Numerical experiments demonstrate that the predicted images contain significantly better visual details than the corresponding input images. The developed approach and the constructed CNN models can provide visual information on the solution of a given problem using only a few iterations without performing the whole iterative solution.