A new technique for the use of Artificial Neural Networks (ANN) for the generalization and visual presentation of the results of experimental studies is proposed. The possibility of using ANN for cases for which their use was previously considered impossible is shown. ANN calculators have been created that summarize the results of experimental studies on the effect of trans-polynorbornene and basalt fiber on the characteristics of a rubber compound based on general-purpose rubbers (isoprene SKI-3, butadiene-methylstyrene SKMS-30ARK and butadiene SKD), which also contained vulcanizing agents (N, N′-dithiodimorpholine, thiuram D), vulcanization accelerators (sulfenamide C, 2-mercapto-benzothiazole), vulcanization activators (zinc white, stearic acid), emollients (industrial oil I-12A, rosin) and antioxidants (acetonanil H, diaphene FP). The rubber mixture was prepared on laboratory rollers LB 320 160/160. Subsequently, the rubber mixture was vulcanized in a P-V-100-3RT-2-PCD press. For the resulting vulcanizates, the physical and mechanical properties and their changes were determined after daily exposure to air and in a standard SZhR-1 hydrocarbon liquid at a temperature of 100 °C. We also studied the change in the mass of vulcanizates after exposure to industrial oil I-20A and water. The dynamic parameters (modulus of elasticity and mechanical loss tangent) of vulcanizates, which characterize their noise and vibration-absorbing properties, were studied on a Metravib VHF 104 dynamic mechanical analyzer. The created ANN calculators allow solving a direct problem - interpolating the dependences of all rubber characteristics on the content of basalt fiber, as well as solving inverse problems - to determine the required content of basalt fiber to create rubber with the required performance properties. The autonomous executable modules of the calculators developed by ANN were made and can be passed to everyone.

The combustion properties of energetic materials have been extensively studied in the scientific literature. With the rapid advancement of data science and artificial intelligence techniques, predicting the performance of solid rocket propellants (SRPs) has become a key focus for researchers globally. Understanding and forecasting the characteristics of SRPs are crucial for analyzing and modeling combustion mechanisms, leading to the development of cutting-edge energetic materials. This study presents a methodology utilizing artificial neural networks (ANN) to create multifactor computational models (MCM) for predicting the burning rate of solid propellants. These models, based on existing burning rate data, can solve direct and inverse tasks, as well as conduct virtual experiments. The objective functions of the models focus on burning rate (direct tasks) and pressure (inverse tasks). This research lays the foundation for developing generalized combustion models to forecast the effects of various catalysts on a range of SRPs. Furthermore, this work represents a new direction in combustion science, contributing to the creation of a High-Energetic Materials Genome that accelerates the development of advanced propellants.