MATHEMATICAL MODELING AND ARTIFICIAL INTELLIGENCE APPROACHES

Abstract

This article analyzes the mutual integration of mathematical modeling and artificial intelligence (AI) approaches and focuses on their application in various fields. Mathematical modeling is a key tool in scientific and engineering research by describing systems and processes in mathematical expressions. However, these models can often be limited in capturing the full properties of complex and dynamic systems. Here, we consider the possibilities of increasing the efficiency of mathematical models using artificial intelligence, especially technologies such as machine learning and neural networks. The article provides detailed information on the theoretical foundations of the joint operation of mathematical models and AI algorithms, as well as how they are applied in real-world systems. For example, the synergistic effect of these two approaches in areas such as disease forecasting in medicine, market analysis in economics, and traffic optimization in transportation is emphasized. The article also examines new opportunities and challenges arising from the integration of mathematical modeling and artificial intelligence approaches, including the prospects for improving data quality, optimizing systems, and improving machine learning processes. As a result, these approaches create opportunities for systems to make high-precision forecasts and make optimal decisions.