Improving the methodology for forecasting complex physical processes based on artificial intelligence and physical modeling

The increasing complexity of physical processes and the growing volume of experimental and computational data have created a need for more accurate and efficient forecasting methods. This study proposes an improved methodology for forecasting complex physical processes through the integration of artificial intelligence (AI) techniques and physics-based modeling. The proposed approach combines the predictive capabilities of machine learning algorithms with the fundamental principles and governing equations of physical systems. This integration enables the development of models that not only learn nonlinear relationships from data but also maintain consistency with established physical laws. The methodology includes data preprocessing, identification of relevant physical parameters, development of a physics-based model, training of an artificial intelligence model, and integration of the two approaches into a hybrid forecasting framework. The proposed methodology is intended to reduce prediction errors, improve model generalization, and increase the interpretability and reliability of forecasts under different physical conditions. The framework can be applied to a wide range of problems, including thermal processes, fluid dynamics, energy systems, mechanical systems, and environmental physics. The study demonstrates the potential of physics-informed and hybrid AI models as an effective approach for forecasting complex physical phenomena and provides a methodological basis for further development of intelligent computational models in modern physics and engineering.