In recent years, the safe operation of energy facilities has increasingly been ensured by probabilistic non-destructive testing systems. This article examines a method for predicting and estimating the number of missed defects by solving an inverse problem. A detailed analysis of indirect manifestations and prediction of an indirect parameter is conducted using the Keras deep learning library, which determines the quantitative characteristics of the facility under study. The results of the study demonstrate encouraging prediction accuracy with easily correctable signs of model overfitting.

Keywords: non-destructive testing, defects, defect detection probability distribution curves, synthetic data for deep learning, regression forecasting, Keras, structural and semantic features, non-linear dependencies