# Introduction {#sec-chap_1_Intro}
In this work, a tool called \glsfirst{cnmc} is further developed.
The overall goal, in very brief terms, is to generate a model, which is able to 
predict the trajectories of general dynamical systems. The model
shall be capable of predicting the trajectories when a model parameter
value is changed. 
Some basics about dynamical systems are covered in 
subsection [-@sec-subsec_1_1_1_Principles] and in-depth explanations about \gls{cnmc} are given in
chapter [-@sec-chap_2_Methodlogy]. \newline 

However, for a short and broad introduction to \gls{cnmc} the workflow depicted in figure @fig-fig_1_CNMC_Workflow shall be highlighted. 
The input it receives is data of a dynamical system or space state vectors for a range of model parameter values. The two main important outcomes are some accuracy measurements and the predicted trajectory for each desired model parameter value.
Any inexperienced user may only have a look at the predicted trajectories to 
quickly decide visually whether the prediction matches the trained data. Since \gls{cnmc} is written in a modular manner, meaning it can be regarded as 
a black-box function, it can easily be integrated into other existing codes or 
workflows. \newline

![Broad overview: Workflow of \gls{cnmc}](../../3_Figs_Pyth/1_Task/1_CNMc.svg){#fig-fig_1_CNMC_Workflow}