Forecasting Chaotic Dynamics Using a Hybrid System

The literature is rich with studies and examples on parameter estimation obtained by analyzing the evolution of chaotic dynamical systems, even when only partial information is available through observations. However, parameter estimation alone does not resolve prediction challenges, particularly when only a subset of variables is known or when parameters are estimated with a significant uncertainty. In this paper, we introduce a hybrid system specifically designed to address this issue. Our method involves training an artificial intelligence system to predict the dynamics of a measured system by combining a neural network with a simulated system. By training the neural network, it becomes possible to refine the model’s predictions so that the simulated dynamics synchronizes with that of the system under investigation. After a brief contextualization of the problem, we introduce the hybrid approach employed, describing the learning technique and testing the results on three chaotic systems inspired by atmospheric dynamics in measurement contexts. Although these systems are low-dimensional, they encompass all the fundamental characteristics and predictability challenges that can be observed in more complex real-world systems.