THE SCIENTIFIC AND THEORETICAL FOUNDATIONS OF MODELING EXPERIMENTAL PROCESSES BASED ON ARTIFICIAL INTELLIGENCE

This article examines the scientific and theoretical foundations of modeling experimental processes using artificial intelligence (AI). It provides a comprehensive analysis of how AI technologies enable the reconstruction of experimental systems in digital environments, the development of virtual models that closely resemble real conditions, and the prediction of probabilistic outcomes under complex interactions. Machine learning, mathematical modeling, statistical analysis, digital twin technology, neural networks, and algorithmic forecasting are discussed as modern methodological tools that significantly enhance research efficiency. The article highlights the relevance and importance of AI-based experimental modeling, identifies existing challenges, explores scientifically grounded solutions, and proposes recommendations for further development.