Mathematical modeling and simulation are not only inevitable ways to better understand the complexity of the world, but they also provide us with fantastically efficient predictive tools.
Numerical simulations are very useful to provide a priori information about phenomena that cannot be reproduced in laboratory experiments. While digital models are increasingly used to assist in decision-making (e.g. in government), scientists in all disciplines also rely on them to understand the evolution of complex phenomena, particularly by relying on data.
The virtuous circle [modelling - simulation/prediction - optimisation - control] is based on the models of the phenomenon under study. Models for real-world systems have been gradually refined, taking into account more heterogeneous variables and more interactions or dependencies between different components or subsystems.
Similarly, artificial systems are becoming increasingly complex, involving a growing number of elements, for example, sensor networks, the Web and the Internet of Things, and electricity grids.