Algorithmic Natural Network Patterns

Humans have a potential tool to understand complex patterns in nature and create rules according to them. Mathematics and algorithmic processes allow us to project complex natural patterns to tangible formulas and geometries. Networks are an example of natural patterns found in nature a lot. The brain’s neural system, spider web, bone tissue, etc., are some remarkable examples. The most efficient way to study these phenomena is to understand their growing pattern. However, in this project, the focus was on geometry and mathematical formula to mimic the form.

Creating bone tissue patterns as random geometry can be achieved by various methods. This pattern can be named “Stochastic Lattice” in the 3D modeling world. There are some related useful plug-ins and software for this purpose. Still, the problem with these tools is that they restrict the random geometry in limited boundaries like cube. In the other word, they are limited to massive volumes. If the user wants to reach a free-form model, a significant process in several steps should be taken to have a desirable result. This process takes time and can be compacted into one step.

This project aims to reduce the time spent creating random patterns by making the process as short and fast as possible.
There were two main objectives for the project:
- How can we apply a random geometry to a preferable FREE-FORM SURFACE as a parametric system?
- How can we reduce the chance of OVERLAPPING POINTS in the hub networks?

This script has been developed to solve mentioned problems in creating natural stochastic patterns. Some advantages and creative ideas were discussed. The project’s main goal is achieved in one component to reduce the computational design time. Furthermore, both objectives have been achieved during this experiment.
For the next stages of development, new parameters can be added like the thickness of the nexuses, the z axis distance from surface in each side, connection type, etc.