A computational model is a mathematical and algorithmic representation of a system that uses computing resources to simulate, predict, or analyse the system’s behaviour under varying conditions. It encodes the entities, state variables, and governing rules of a phenomenon into executable form so that experiments can be run in silico rather than physically. Computational models underpin scientific simulation, engineering design, and the predictive components of spatial and physical computing.
Overview
- Computational models exist on a spectrum from analytical closed-form representations to large-scale numerical solvers. They capture state, dynamics, and constraints, then advance the system through time or parameter space to reveal emergent behaviour that is difficult to derive by hand.
- Their value lies in enabling controlled, repeatable experimentation. Engineers and scientists vary inputs, observe outputs, and iterate on designs without the cost or risk of physical prototyping, making models central to modern research and product development.
Key aspects
- Discretisation reduces continuous phenomena to finite, computable elements such as grids, meshes, or time steps.
- Validation and verification establish that a model faithfully represents reality and is implemented correctly.
- Parameterisation exposes tunable variables that calibrate the model to observed data.
- Fidelity trade-offs balance accuracy against computational cost and runtime.
Applications
- Physics Engine components rely on computational models of dynamics and collision.
- Digital Twin systems mirror physical assets through continuously updated models.
- Autonomous Vehicle Testing uses computational models to simulate sensors and environments.
- Scientific simulation models climate, fluids, structures, and biological processes.