Gradient evaluation for neural-networks-based electromagnetic optimization procedures

This paper extends the use of a neural network (NN) approximating a function, to the evaluation of the gradient of the same function. This is done without any extra training of the network. The evaluation of the function's gradient is used in NN-based optimization procedures in order to speed up the convergence and to maintain the overall accuracy.