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

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

Abstract

This article focuses on the application of pre-training methods in the task of synthesizing surrogate models. The article emphasizes that pre-training significantly improves the accuracy of surrogate models and speeds up their creation process. The authors examine pre-training's impact on various aspects of surrogate modeling of a gas turbine unit that is part of a gas turbine electric power station, such as reducing computational costs, improving the approximation of complex processes, and optimizing the model synthesis procedure. The work demonstrates specific examples that clearly show how the use of pre-training can significantly improve the performance of surrogate models and optimize the development process. Thus, the authors convincingly argue that pre-training is a key tool for increasing the efficiency of surrogate modeling, capable of significantly reducing the time, costs, and efforts required for the development and use of surrogate models in the energy sector.