# Bloch oscillations in two-dimensional crystals: Inverse problem

## TL;DR

This paper uses artificial neural networks to classify and analyze Bloch oscillation signals in two-dimensional crystals, achieving high accuracy in identifying lattice parameters and electric field orientation.

## Contribution

It introduces a novel ANN-based method for inverse classification of Bloch oscillations in 2D crystals, a pioneering approach in this area.

## Key findings

- Achieved 96% classification accuracy.
- Demonstrated feasibility of ANN for inverse problem in 2D BO.
- Showed potential for improvement with more training and computational resources.

## Abstract

Within an artificial neural network (ANN) approach, we classify simulated signals corresponding to the semi-classical description of Bloch oscillations on a two-dimensional square lattice. After the ANN is properly trained, we consider the inverse problem of Bloch oscillations (BO) in which a new signal is classified according to the lattice spacing and external electric field strength oriented along a particular direction of the lattice with an accuracy of 96%. This approach can be improved depending on the time spent in training the network and the computational power available. This work is one of the first efforts for analyzing the BO with ANN in two-dimensional crystals.