It assists the user to formulate a suitable strategy for striking a balance between accuracy and effectiveness and to tune the model to accomplish satisfactory modeling of real phenomena.
In this last section, we mention some kind of problems that model different real phenomena that, as we will see, can be presented under the same formulation.
Integro-differential equations have become more and more important in some mathematical models of real phenomena, especially in control, biological, medical, and informational models.
The stochastic functional differential equations with state-dependent delay have many important applications in mathematical models of real phenomena, and the study of this type of equations has received much attention in recent years.
While attempting to calculate such effects for a large complex system like a city, an advanced multidisciplinary approach is needed to overcome difficulties in modeling correctly real phenomena while maintaining computational transparency, reliability, interoperability and efficiency across different levels of analysis.
Theories can model real-world phenomena, and can do so more or less correctly.
The Riemann Liouville derivative has certain disadvantages when trying to model real-world phenomena with fractional differential equations.
Recently, stochastic differential equations with jumps (SDEwJs) are becoming increasingly used to model real-world phenomena in different fields, such as economics, finance, biology, and physics.
But it turns out that the Riemann-Liouville derivatives have certain disadvantages when trying to model real-world phenomena with fractional differential equations.
