Deep learning regression for inverse quantum scattering

Abstract

In this work we study the inverse quantum scattering via deep learning regression, which is implemented via a Multilayer Perceptron. A step-by-step method is provided in order to obtain the potential parameters. A circular boundary-wall potential was chosen to exemplify the method. Detailed discussion about the training is provided. A investigation with noisy data is presented and it is observed that the neural network is useful to predict the potential parameters.