Continuing on the theme of using machine learning to speed up electromagnetic simulations, we've got some new work to share! This paper goes beyond the 2D example previously explored and now looks at full-wave 3D solutions. Most importantly, we wanted to show that this is possible with geometry generalizations. For example, many papers pass things like via drill diameter and other parameters directly as input to their neural networks. We instead mesh the geometry into a 3D rectangular grid and pass this as input to the neural network instead. The result was quite impressive!

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