Particle-based

Describing a system, model, or approach that relies on individual discrete entities or 'particles' as the fundamental building blocks or units of analysis. These particles can represent a wide range of entities, from physical objects like grains of sand or atoms to abstract elements like agents in a simulation or data points in a statistical analysis. particle-based methods often involve simulating the interactions and movements of these individual units to understand the emergent behavior of the system as a whole. The behavior is determined by properties that can be continuous or discrete in nature.

Particle-based meaning with examples

• Researchers developed a particle-based model to simulate the spread of a disease in a city. Each particle represents an individual, and their interactions, like proximity, govern the transmission of the disease. This model allowed them to predict infection rates under different quarantine scenarios to see the emergent behavior of the system as a whole.
• In computational fluid dynamics, particle-based methods such as Smoothed Particle Hydrodynamics (SPH) are used to simulate fluid flow. Instead of using a grid, the fluid is represented by a collection of particles, whose interactions determine fluid behavior, helpful where free surfaces and complex boundaries are involved.
• The development of particle-based rendering techniques allowed game developers to create realistic simulations of fire, smoke, and explosions. Tiny particles are used and their movement and interaction are modeled to emulate real-world effects. Their emergent behavior allows for visually stunning results in games and special effects.
• Scientists used a particle-based approach to study the formation of galaxies, simulating the gravitational interactions between countless particles of dark matter and gas. The emergent patterns allowed them to better understand the large-scale structure of the universe and the role of dark matter in the same.
• Machine learning models use particle-based algorithms that sample parameters to find the best ones. These particles iteratively improve the quality of a model. The emergent behavior of the algorithm as a whole helps scientists find the best possible solutions for difficult problems.