Dynamic Clouds and Landscape Generation: Morphing and Evolutionary Processes
My previous article focused on map generation in 3D, and also features a fascinating video, see here. In this article, while focusing on 2D, I provide a simple introduction to evolutionary processes in the context of synthetic data and terrain generation. Not just terrains: depending on the color palette, other processes such as storm formation can be simulated with the same algorithm.
The focus is on stationary processes. The analogy with random walks and Brownian motions is striking. Despite the simplicity, the systems modeled here are a lot more complex than your typical Brownian motion. You can compare it to time-continuous time series, where each observation (synthetically generated here) is an image. This article will appeal to practitioners looking for more sophisticated modeling tools, that mimic natural phenomena. It will also appeal to machine learning professionals looking for professional Python code, the kind of code typically not taught in any class or textbook, and not found on the Internet. It offers a fun application to learn scientific computing.
I also explain how to produce animated data visualizations in Python (MP4 videos) featuring 4 related sub-videos in parallel, progressing at various speeds. In particular, the video shows the probabilistic evolution of a system from A to B, compared with morphing the starting configuration A into the final state B. In the end, this article can serve as an introduction to chaotic dynamical systems.
Read the full article here.
