Continuous simulation

Since that time continuous simulation has been proven invaluable in military and private endeavors with complex systems.

However, in digital computing, real numbers cannot be faithfully represented and differential equations can only be solved numerically with approximate algorithms (like the method of Euler or Runge–Kutta) using some form of discretization.

For that reason, a continuous simulation of sales does not faithfully model reality but may nevertheless capture the system's dynamics approximately.

These equations define the peculiarity of the state variables, the environment factors so to speak, of a system.

[6] The set of differential equations can be formulated in a conceptual model representing the system on an abstract level.

A continuous simulation of population dynamics represents an approximation effectively fitting a curve to a finite set of measurements/points.

Numerical integration methods such as Runge Kutta, or Bulirsch-Stoer could be used to solve this particular system of ODEs.

To speed creation of continuous simulations you can use graphical programming software packages like VisSim or Simcad Pro.

The packages provide options for integration method, step size, optimization method, unknowns and cost function, and allow for conditional execution of subsystems to speed execution and prevent numerical errors for certain domains.

Such graphical simulation software can be run in real-time and used as a training tool for managers and operators.