Exploring Fresnel diffraction at a straight edge with a neural network

TL;DR

This paper demonstrates how undergraduate students used neural networks to study Fresnel diffraction at a straight edge, providing educational insights into wave optics and machine learning integration.

Contribution

It introduces a novel educational approach combining classical wave optics with neural network techniques for student engagement.

Findings

Students learned the steps of machine learning processes.

Neural networks effectively modeled Fresnel diffraction.

Enhanced understanding of wave optics through machine learning.

Abstract

We describe a research project carried out with a group of undergraduate physics students and aimed at exploring the use of a neural network to study a classical problem in wave optics whose analytical solution is well known: the diffraction of light by the straight edge of an opaque semi-infinite screen. Through this exposure to machine learning, the students were able to appreciate the basic steps involved in a machine-learning process.