Overview
Strange attractors are a specific type of dynamical system that creates an attractor with a fractal structure. In this research I explored applications of strange attractors in generative design by developing mapping algorithms to apply the features of strange attractors to 3D geometry.
Process
Attractor systems were programmed in Houdini and once initial conditions were seeded, geometry was generated using a solver. Generated point values are normalized for single-point initialization and the most recently generated values for each point with multi-point initialization. An output seed and mapping variables apply those values to desired geometry.
Output Mappings
Examples of output mappings include a basic heightmap conversion from multi-point initialization and dynamic radius control with variance controlled by the resolution of the attractor solver. Both sets of outputs can be generated with the same attractor seed but different solver resolutions, demonstrating variance control with this mapping system.
Animating Outputs
Caching generated solver values allows for animated output mappings. This method is more computationally expensive than traditional simplex or Perlin noise algorithms, but yields visually unique results with fairly predictable repetitive patterns while leveraging the fractal nature of strange attractors.
This research was presented at CUR's National Conference on Undergraduate Research and as a plenary presentation at Harvard's National Collegiate Research Conference in 2021.