# Video Examples

https://youtu.be/Y-5ffl5_7AI?t=75

https://youtu.be/dkP8NUwB2io?t=244

# Inspiration

Essentially, these two videos depict the fish movement, but more precisely, it is based on an invisible path that is created from the connection of multiple fish neurons.

“Soon, so many neurons are interacting in so many different ways at once, that the system becomes chaotic.”

Generative systems often include chaotic behaviour. Like the fish, while the neurons of the fish is a simplistic system following a strict sequence of cause and effect, there is a dynamic nonlinear and unpredictability element.

# Generative Sketch

Therefore, for my chosen medium, I will be using Processing whereby I will be exploring the feasibility of particles to simulate the movement of the fish.

To do so, I will be learning ways to create particle simulation and on how to move particles in a unison manner. The unpredictability element comes from the procedural coding,

1. whereby the manipulation of the number of particles,
2. the characteristics of the behaviours of the particle (Separation, Alignment, Cohesion, Speed)
3. and the size of the boundary will offer a new result.

One challenge I foresee will be the manipulation of the characteristics of the particle to achieve a desirable outcome, such as moving the particles in a unison manner. However, that is part of the learning journey and I look forward to the challenges that lie ahead.

## Generated Physics System: Simulated Physical Behaviour & Autonomous Particle-Interactions

Generative Physics Systems

# Generative Physics System

Generative Physics system by Patrick Huebner, which utilises simulated physical behaviour and autonomous particle-interactions; one way to imagine it would be like having a bunch of tadpoles swimming in a pond. What I found interesting about this work was the infinite amount of unique details that can be discovered and created in each simulation. Furthermore, the incorporation of physics to generate randomness results struck me of how we can incorporate basic math equations into art. Another notable factor is that this was coded and compiled using processing, which amazes me of how much this software can deliver.

Generative art is defined as a conscious approach (clear algorithm) but open uncontrollability or unpredictability that surprises. For this case, the artwork has a particle interaction simulation with an explicit algorithm of 4 rules. First, the particles follow a global vector of attraction (gravity). Second, it adheres to set physical constants like friction and restitution. Third, if a particle collides with another particle, it will move into the opposite direction. Last, if it collides with a set boundary, move into the opposite direction. While it is a simple set of rules based on physics, the results quickly become complex; which gives an impression as though the particles are alive and each time the simulation is played a new work is created.

How does this relate to me? While I am not an ADM student nor am I a Computer Science student, I do enjoy art as well as programming. Before learning about Generative Art, I have always seen programming as a flow of logical sequence which is unrelated to art. However, this artwork has shown me the mix between the two, and how we can harness technology to achieve inspirations and creativity. Additionally, I am also taking the module “Interactive art” at NIE, so perhaps I hope to take away something I learnt here to that module too.