Deep learning regression for inverse quantum scattering

TL;DR

This paper explores using deep learning regression with a Multilayer Perceptron to solve inverse quantum scattering problems, demonstrating its effectiveness even with noisy data for potential parameter prediction.

Contribution

It introduces a step-by-step deep learning approach for inverse quantum scattering and shows its robustness with noisy data, which is a novel application.

Findings

Neural network accurately predicts potential parameters

Method works with noisy data

Deep learning offers a viable solution for inverse quantum problems

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.