Grigory Kilin¹ , Boris Kavalerov¹ , Artem Suslov¹* , and Ilya Tyatenkov¹

¹ Perm National Research Polytechnic University, 29, Komsomolsky prospect, Perm, 614990, Russian Federation

Abstract

The article is devoted to the current task of selecting pretraining parameters for the synthesis of surrogate models, which is a key factor in creating high-performance models of complex technological objects. During the study, the authors conduct a systematic analysis of various parameters and their interactions, including determining the optimal number of training iterations, the number of trainable layers, and the number of neurons in these layers. Thanks to this approach, the results of the presented study can significantly improve the accuracy and efficiency of surrogate models, which in turn leads to simplification and acceleration of the process of their development and application in various fields of science and engineering.