INVESTIGATION OF RADIOISOTOPES POPULATION DYNAMICS BY THE HELP OF MODELING AND ARTIFICIAL INTELLIGENCE

This paper investigates the alpha-decay chain reaction of Uranium-238 ([Formula: see text]) into Thorium-234 ([Formula: see text]) and Radium-226 ([Formula: see text]) through a combination of computational simulations and theoretical analysis. The study focuses on the role of varying Uranium-238 and decay constants on the final quantity. Results indicate that higher initial Uranium-238 quantities yield increased final quantities of both Thorium-234 and Radium-226, with decay constants significantly influencing the system’s behavior. A fixed-point technique is employed to ensure the existence of solutions, confirming the system’s stability and convergence to steady-state solutions. Additionally, the potential of artificial intelligence (AI) techniques is discussed, highlighting their utility in optimizing decay models and improving computational efficiency. The integration of AI offers deeper insights into parameter optimization and outcome prediction for complex decay systems. These findings contribute to the understanding of radioactive decay dynamics and provide a framework for more advanced computational models in nuclear science, as a process innovation.