Stochastic nonlinear dynamics pattern formation and growth models
2007
Stochastic Nonlinear Dynamics in Pattern Formation and Growth Models
publication
Evidence: moderate
Author Information
Author(s): Yaroslavsky Leonid P
Primary Institution: Department of Interdisciplinary Studies, Faculty of Engineering, University of Tel Aviv
Hypothesis
Can stochastic evolutionary growth and pattern formation models be unified and described using nonlinear dynamic systems?
Conclusion
The study demonstrates that simple algorithmic models can generate a wide variety of natural patterns through nonlinear dynamics.
Supporting Evidence
- Simple algorithmic models can generate complex patterns found in nature.
- Models can imitate biological growth processes.
- Stochastic models can produce a variety of textures and patterns.
Takeaway
This study shows how we can use math to create patterns that look like those found in nature, like fingerprints and zebra stripes.
Methodology
The study uses algorithmic models of nonlinear dynamic systems with feedback to generate patterns.
Digital Object Identifier (DOI)
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