Quick description
The evolution of a system can be regarded as a succession of states with regular time intervals (discretization), which the differences, since they are always justified, can be controlled.

Modeling consists in defining rules of evolution for intervals of time defined over a given period, then to calculate and to reproduce this evolution.
Time intervals and period define the temporality of the model; rules are actions, contained in flows.

Temporality
Modeling based on discrete or continuous values of flows is of sequential decision type emanating from actions individually planned through seven temporal parameters :
- rate, chronology, start, repeat, interval, type of cycle and filtered cycles.
The out of synch of floating cycle and circularity are possible.
The scales of time are virtual, defined for basic or floating cycle.
The model is calculated for a defined number of cycles.

Actions (in flows)
There return values modify stocks, they are :
- constant, temporal array, cloud (no temporal array with double entry) and procedure.
In procedures, all the needed code is possible
Functions of this code allow to dialog with other software, database, etc. :
- Automation, Com, Dcom, Rpc, DotNet, Http, Socket, Ftp, J2eee, ...
- all functions are written with power language in French or in English in a very easy syntax, like natural language, they will be dynamically compiled.

Results analyze
During the phase of calculation all the results are stored in the data base of studied model.
They are then naturally displayed in the model through its elements, stocks and flows.
Dynamic charts and 3D or 4D animations can be encrusted in the model.
TRUE notation

Strong points
Creating models
The graphic user interface, intuitive and wysiwyg, permits to create, modify and analyze models.

Dynamic optimization
During the phase of calculation, actions can dynamically loop temporally to reach the expected results. This functionality is called retro-calculation; it permits overlapped looping, training and thus artificial intelligence.

Vectoring
Elements can be vectored by vectors made up of domains.
A subset of a vectored model could thus be associated to various cases of figures.
Other vectors can be created by making the Cartesian product of normal vectors.
One can thus combine up to twenty vectors, but this limit can be increased.

Charts
They represent the values of the elements over the simulated period.

Multibody Dynamics Simulation, Procedural Animation, 3D or 4D animations
3D animations can be created using the 3D modeler : it calls primitives of the OpenGL graphic library.
If one or more primitives are drived by the variables of the model, the 3D animation becomes a 4D animation.
This is called procedural animation.
The primitives can be assembled, repeated and separately initialized.

Equation and integrity
For any studied model and at any time during the simulated period, the sum of all the stocks will remain equal to the sum of the initial values of those stocks, because the values calculated by the actions are transferred between one source stock and one target stock.

Collateral effects
Since the principle used is that of the dynamic balance of the systems, any variation of the value of one stock must be compensated, giving prominence thus obligatorily the collateral effects caused by this variation.

Diachrony and synchrony
Synchrony is the study of all the relations between the elements at a given time.
Diachrony is the evolution of a system in time, elements by elements.
These two characteristics are possible simultaneously by the dynamic graphic restitution of the values of the elements of the model.