Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective

TL;DR

This paper presents a zero-coding neural network approach to simulate fungal growth and containment, enabling artists to generate realistic fungal patterns and control their spread into complex shapes.

Contribution

It introduces a novel zero-coding, neural network-driven cellular automaton for fungal morphology simulation and dynamic containment, bridging graphics generation and bio-art.

Findings

Neural network automaton accurately replicates real fungal spread behaviors.

Dynamic containment with lasers enables precise control of fungal growth.

Fungal patterns successfully formed complex shapes in real-world experiments.

Abstract

Fungal simulation and control are considered crucial techniques in Bio-Art creation. However, coding algorithms for reliable fungal simulations have posed significant challenges for artists. This study equates fungal morphology simulation to a two-dimensional graphic time-series generation problem. We propose a zero-coding, neural network-driven cellular automaton. Fungal spread patterns are learned through an image segmentation model and a time-series prediction model, which then supervise the training of neural network cells, enabling them to replicate real-world spreading behaviors. We further implemented dynamic containment of fungal boundaries with lasers. Synchronized with the automaton, the fungus successfully spreads into pre-designed complex shapes in reality.