This is essentially a compositional pattern-producing network (CPPN) except it makes abstract patterns instead of meaningful shapes. Inputs: pixel coordinates, mouse/touch coordinates, cos(time). Output: RGB values. Activations: sin, cos, tanh, linear. Weights: random.
Detailed explanation and live demo in my blog
TensorFlow.js utilizes WebGL to make calculations faster. It can also use CPU only, but you'll get much better framerates on a GPU-equipped device.
Edit (2020-03-01): I added vanilla_js.html which uses only plain js without any libraries.
The following two resources introduced me to abstract art generation with CPPNs.
https://www.deeplearning.ai/ai-notes/optimization/.
https://tehnokv.com/posts/visualizing-audio-with-cppns/
I "borrowed" the idea of using cos(time) to generate dynamic patterns, but implemented the CPPN in js from scratch on my own. The idea to use mouse/touch coordinates as additional inputs came from my friend, Igor Guzman. It was my idea to add selection of activation functions and amplitude sliders for additional interactivity.