MODELS IN AI USING DIFFERENTIAL EQUATIONS FORMATTED

Abstract

In recent years, differential equations have become an essential tool in artificial intelligence (AI) for modeling dynamic systems and forecasting complex processes. This study investigates the integration of ordinary and partial differential equations (ODEs and PDEs) into AI-based forecasting models. The paper proposes a hybrid mathematical model that combines neural networks and differential equations to improve prediction accuracy in time-series analysis. Experimental results demonstrate that incorporating differential equations enhances the model’s interpretability and reduces forecasting errors compared to purely data-driven methods.