Simulating natural systems with code. From vectors and forces to neural networks and genetic algorithms, each chapter rebuilt as an interactive lesson with live Canvas simulations.
Euclidean vectors, addition, subtraction, magnitude, normalization, and the PVector class.
Newton's laws, gravity, friction, drag, and applying multiple forces to moving objects.
Angles, angular motion, trigonometry, pendulums, springs, and wave patterns.
Emitters, lifespans, ArrayLists, inheritance, polymorphism, and forces on particles.
Box2D, toxiclibs, rigid bodies, joints, springs, and collision detection.
Steering behaviors: seek, flee, arrive, wander, flow fields, path following, and flocking.
Wolfram's elementary CA, the Game of Life, and emergent complexity from simple rules.
Recursion, self-similarity, the Koch curve, fractal trees, L-systems, and the Mandelbrot set.
Genetic algorithms, fitness functions, selection, crossover, mutation, and evolving phrases.
The perceptron, weighted inputs, supervised learning, and training with gradient descent.