## 1 Introduction
The use of the CFD and FEM methods for the temperature field prediction during the design phase can take significant time not only due to the model creation and boundary condition application but also due to the solution process itself, which can take for complex or detailed FEM models several hours and in general, these calculations must be performed many times, particularly during the optimization process. Therefore, the interesting solution how to eliminate the time demands seems to be the application of the novel method, based on the integration of the artificial neural networks (ANN), which would decrease the solution time significantly. The ANNs are nowadays used mainly in electrical and electronic engineering; however, they can also be applied for the solution of mechanical engineering tasks. They can be used, for example, for the prediction of the various physical and mechanical parameters of the different technical systems or their elements in industrial applications. For example, in the study [19], research is focused on the investigation of the mas detection and its impact on the simple beam, which is possibly applicable on the turbine blade. In this case, the ANN is used to predict mechanical parameters. The ANN can also be used for the stress, fatigue lifetime, or creep prediction of the jet engine turbine parts [20; 21]. Thereare some applications of the ANN for the temperature, fatigue life or other parameters determination [22; 23; 24; 25]. As it was already mentioned, calculation of the temperature fields for the turbine blade using the CFD and FEM methods is not a trivial issue, mainly due to the jet engine operation in multiple types of regimes. For instance, in the case of the one regime calculation, the analysis would take, for example, an hour [26]. In the load case of 100 operations of the jet engine, the analysis would take 100 h, and if there is a need to predict temperature fields of the turbine blade in real time, completely different and more complex methods must be selected [27; 28]. Our research is originally applied for the temperature prediction of the turbine blade using the integration of the ANN methodology and the data obtained from the CFD analyses, utilizing the advantages of both methods. The originality of the paper lies in the prediction of temperatures using ANN, which can, for some cases, replace the CFD method. The developed method was consequently applied and verified on the iSTC-21v small jet engine [29; 30; 31]. This jet engine with the designation iSTC-21v is the experimental single shaft jet engine, which has been a part of the continuous ongoing research performed in the Laboratory of Intelligent Control Systems of Aircraft Engines in Faculty of Aeronautics of the Technical University of Kosice [32; 33; 34]. Thanks to the long-term research performed in our laboratory on the iSTC-21v jet engine dealing mainly with its control systems, for optimization, of which many additional characteristics such, for example, magnetic, temperature, pressure, etc. had to be measured and studied, the engine and its characteristics were analyzed in details. The article deals with the following research based on the numerical approach followed by the whole series of simulations. One part of this research presented in the article is focused on the integration of the existing measured characteristics, which served as a basis for the numerical simulations. Moreover, thanks to the knowledge and experience obtained during the design of control algorithms, the cybernetic approach was implemented. In this way, it is possible to improve the efficiency of the design or necessary modification of the component without the need for time-consuming calculations because the methodology presented in the article based on the artificial neural networks can be used almost in real time.