From 4d2574c23675db32c0d0ecd7dc08dc57e835266c Mon Sep 17 00:00:00 2001 From: shiffman Date: Mon, 26 Feb 2024 22:20:35 +0000 Subject: [PATCH] Notion - Update docs --- content/06_libraries.html | 4 +++- content/07_ca.html | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/content/06_libraries.html b/content/06_libraries.html index 208a4e7..803410e 100644 --- a/content/06_libraries.html +++ b/content/06_libraries.html @@ -1393,7 +1393,9 @@ class Particle extends VerletParticle2D { Figure 6.13: Soft-body simulation designs
Figure 6.13: Soft-body simulation designs
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As the figure shows, a string can be simulated by connecting a line of particles with springs; a blanket can be simulated by connecting a grid of particles with springs; and a cute, cuddly, squishy cartoon character can be simulated with a custom layout of particles connected with springs. It’s not much of a leap from one to another.

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As the figure shows, a string can be simulated by connecting a line of particles with springs; a blanket can be simulated by connecting a grid of particles with springs; and a cute, cuddly, squishy cartoon character can be simulated with a custom layout of particles connected with springs. It’s not much of a leap from one to another.

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A String

I’ll begin by simulating a soft pendulum—a bob hanging from a flexible string instead of a rigid arm. As it happens, Toxiclibs.js offers a convenient ParticleString2D class that creates a string of particles connected by springs in a single constructor call. However, for demonstration purposes, I’ll create my own particle string by using an array and a for loop. This way, you’ll gain a deeper understanding of the system, enabling you to create your own custom designs beyond a single string in the future.

First, I need an array of particles. I’ll use the same Particle class built in Example 6.11:

diff --git a/content/07_ca.html b/content/07_ca.html index 1143fc8..8c2b90e 100644 --- a/content/07_ca.html +++ b/content/07_ca.html @@ -27,11 +27,11 @@
  • Each cell has a neighborhood. This can be defined in any number of ways, but it’s typically all the cells adjacent to that cell.

    • It’s important to stress that the cells in a CA don’t refer to biological cells (although you’ll see how CA can mimic lifelike behavior and have applications in biology). Instead, they simply represent discrete units in a grid, similar to the cells in a spreadsheet (as in Microsoft Excel). Figure 7.1 illustrates a CA and its various characteristics.

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    The second CA feature I listed—the idea that a cell’s state can vary over time—is an important new development. So far in this book, the objects (movers, particles, vehicles, boids, bodies) have generally existed in only one state. They might have moved with sophisticated behaviors and physics, but ultimately they remained the same type of object over the course of their digital lifetime. I’ve alluded to the possibility that these entities can change over time (for example, the weights of steering “desires” can vary), but I haven’t fully put this into practice. Now, with CA, you’ll see how an object’s state can change based on a system of rules.

    Figure 7.1: A 2D grid of cells, each with a state of on or off. A neighborhood is a subsection of the large grid, usually consisting of all the cells adjacent to a given cell (circled).
    Figure 7.1: A 2D grid of cells, each with a state of on or off. A neighborhood is a subsection of the large grid, usually consisting of all the cells adjacent to a given cell (circled).
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    The second CA feature I listed—the idea that a cell’s state can vary over time—is an important new development. So far in this book, the objects (movers, particles, vehicles, boids, bodies) have generally existed in only one state. They might have moved with sophisticated behaviors and physics, but ultimately they remained the same type of object over the course of their digital lifetime. I’ve alluded to the possibility that these entities can change over time (for example, the weights of steering “desires” can vary), but I haven’t fully put this into practice. Now, with CA, you’ll see how an object’s state can change based on a system of rules.

    The development of CA systems is typically attributed to Stanisław Ulam and John von Neumann, who were both researchers at the Los Alamos National Laboratory in New Mexico in the 1940s. Ulam was studying the growth of crystals, and von Neumann was imagining a world of self-replicating robots. You read that right: robots that can build copies of themselves.

    Von Neumann’s original cells had 29 possible states, so perhaps the idea of self-replicating robots is a bit too complex of a starting point. Instead, imagine a row of dominoes; each domino can be in one of two states: standing upright (1) or knocked down (0). Just as dominoes react to their neighboring dominoes, the behavior of each cell in a CA is influenced by the states of its neighboring cells.

    This chapter explores how even the most basic rules of something like dominoes can lead to a wide array of intricate patterns and behaviors, similar to natural processes like biological reproduction and evolution. Von Neumann’s work in self-replication and CA is conceptually similar to what’s probably the most famous CA, the Game of Life, which I’ll discuss in detail later in the chapter.