Performance of artificial neural networks in an inverse problem of laser beam diagnostics

TL;DR

This paper evaluates the effectiveness of artificial neural networks in solving an inverse heat flux problem related to laser beam diagnostics, comparing their performance with traditional methods.

Contribution

It introduces a neural network-based approach for identifying heat flux distributions in laser diagnostics and optimizes hyperparameters for improved accuracy.

Findings

Neural networks successfully identify heat flux distributions.

Comparison shows neural networks outperform traditional methods.

Optimized hyperparameters enhance neural network accuracy.

Abstract

Results are presented for the numerical verification of a method devised to identify an unknown spatio-temporal distribution of heat flux that occurs at the surface of thin aluminum plate, as a result of pulsed, high-power laser beam excitation. The presented identification of boundary heat flux function is a part of newly-proposed laser beam profiling method and utilizes artificial neural networks trained on temperature distributions generated with the ANSYS Fluent solver. The paper focuses on the selection of the most effective neural network hyperparameters (Keras, Tensorflow) and compares the results of neural network identification with Levenberg-Marquardt method used earlier and discussed in our previous articles.