Comparative analysis of residual minimization and artificial neural networks as methods of solving inverse problems: Test on model data

Abstract

This study compares perceptron type neural network and residual minimization for solving inverse problems, at the example of a model inverse problem. Stability of both methods against noise in data was investigated. The conclusion about limited applicability of residual as a criterion of the solution quality has been made.