Mathematical modeling and simulation are not the only ways to better understand the complexity of the world, but they also provide us with fantastically efficient predictive tools.
![Modelamiento](/sites/default/files/2020-03/modelamiento.jpg)
Numerical simulations are useful to provide information about phenomena that cannot be simulated in laboratory experiments. While digital models are increasingly used to assist in decision-making, scientists in all disciplines also rely on them to understand the evolution of complex phenomena, in particular by relying on data.
The virtuous circle [modeling - simulation/prediction - optimization - control] is based on the models of the phenomenon under study. Models for real-world systems have been gradually refined, taking into account more diverse variables and more interactions or dependencies between different components or subsystems.
Analogously, 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.
Featured Projects
![Odortracking](/sites/default/files/styles/bloc_grid/public/2020-04/odortracking_1.png?itok=vLSCCLJb)
Odor Tracking
Development of a web interface that allows the execution of a mathematical model for the dispersion of odor emissions from a meat processing plant.
![PROCYCLA](/sites/default/files/styles/bloc_grid/public/2020-04/foto-procycla.png?itok=u_oJqFk7)
Procycla
Support for the implementation of a mechanical model of the anaerobic digestion process and development of a web application.