This paper introduces particle systems, a method for modeling fuzzy objects such as fire, clouds, and water. Particle systems represent an object as a cloud of primitive particles that define its volume. Over time, particles are generated, move, and die within the system. This approach allows for the representation of motion, form changes, and dynamics not possible with classical surface-based methods. Particles can be motion-blurred, avoiding temporal aliasing and strobing. Stochastic processes control the generation and behavior of particles. The paper discusses the advantages of particle systems over traditional surface-based techniques. Particles are simpler primitives, allowing for more complex images in less computation time. They are also easier to motion-blur, which is important for removing temporal aliasing. The model is procedural and controlled by random numbers, reducing the need for extensive human design. Particle systems can adjust detail based on viewing parameters and can represent dynamic, "alive" objects that change over time. The paper presents applications of particle systems, including modeling fire in the Genesis Demo sequence from Star Trek II: The Wrath of Khan. It describes how particle systems were used to create the fire element, with a two-level hierarchy of particle systems generating expanding concentric rings. The system uses stochastic processes to control particle generation, attributes, and dynamics. Particles are motion-blurred, and their colors and transparency are controlled by mean values and variances. Other applications include modeling fireworks, line-drawing explosions, and grass. Particle systems are used to create dynamic, realistic effects in computer graphics. The paper also discusses ongoing research into particle systems, including modeling clouds and improving rendering efficiency. The paper concludes that particle systems are effective for modeling fuzzy objects and dynamic phenomena. They are procedural, stochastic, and adaptable, making them promising for future research and applications in computer graphics.This paper introduces particle systems, a method for modeling fuzzy objects such as fire, clouds, and water. Particle systems represent an object as a cloud of primitive particles that define its volume. Over time, particles are generated, move, and die within the system. This approach allows for the representation of motion, form changes, and dynamics not possible with classical surface-based methods. Particles can be motion-blurred, avoiding temporal aliasing and strobing. Stochastic processes control the generation and behavior of particles. The paper discusses the advantages of particle systems over traditional surface-based techniques. Particles are simpler primitives, allowing for more complex images in less computation time. They are also easier to motion-blur, which is important for removing temporal aliasing. The model is procedural and controlled by random numbers, reducing the need for extensive human design. Particle systems can adjust detail based on viewing parameters and can represent dynamic, "alive" objects that change over time. The paper presents applications of particle systems, including modeling fire in the Genesis Demo sequence from Star Trek II: The Wrath of Khan. It describes how particle systems were used to create the fire element, with a two-level hierarchy of particle systems generating expanding concentric rings. The system uses stochastic processes to control particle generation, attributes, and dynamics. Particles are motion-blurred, and their colors and transparency are controlled by mean values and variances. Other applications include modeling fireworks, line-drawing explosions, and grass. Particle systems are used to create dynamic, realistic effects in computer graphics. The paper also discusses ongoing research into particle systems, including modeling clouds and improving rendering efficiency. The paper concludes that particle systems are effective for modeling fuzzy objects and dynamic phenomena. They are procedural, stochastic, and adaptable, making them promising for future research and applications in computer graphics.