Computer simulation

Computer simulation is the running of a mathematical model on a computer. This model is designed to represent the behavior of, or the outcome of, a real-world or physical system. By comparing the results of these models to real-world outcomes, we can determine how reliable they are.

Computer simulations have become very useful for studying many natural systems. They help scientists in fields like physics, astrophysics, climatology, chemistry, biology, and manufacturing, as well as human systems in economics, psychology, social science, health care, and engineering. Simulations can help us explore new technology and estimate how well very complex systems work.

These simulations are done by running computer programs. Some are small and quick, while others are very large and take hours or days to complete, using many computers together. For example, in 1997, a simulation of a desert battle involving 66,239 tanks and vehicles was done around Kuwait. Other big simulations include models of a billion atoms, a protein-making structure called the ribosome, and even the entire human brain at a very detailed level.

Simulation versus model

A model is a set of rules or equations that describe how something works. A computer simulation is when we use a computer to run these rules or equations to see what might happen in real life. So, when we talk about simulation, we mean actually running the model on a computer to get results. We say we "build a model" and then "run a simulation" to see what the model predicts.

History

Computer simulation grew alongside computers, starting with big projects in World War II. One early use was during the Manhattan Project to help understand how nuclear detonation works. This was done by simulating 12 hard spheres using a special method called a Monte Carlo algorithm.

Today, computer simulations help us study systems that are too complex to solve with simple math. They let us see many possible outcomes for a problem when checking every single possibility would be too hard or impossible.

Data preparation

Simulations and models need different amounts of information to work. Some need just a few numbers, like measuring electricity on a wire. Others need huge amounts of data, like for weather and climate models.

Data can come from many places: sensors, control tools, information typed in by a person, results from other processes, or outputs from other simulations. Data might be built into the simulation, entered when the simulation starts, or provided while it runs.

Because simulations are so different, there are many special languages made for them. One well-known language is Simula. There are now many others.

Systems that use outside data must be careful about what they receive. Computers can easily read numbers from files, but it’s harder to know how correct those numbers are. Often, these numbers come with “error bars,” showing the smallest and largest possible values. Because computers aren’t perfect, mistakes can add up, so it’s important to check that the simulation’s results are still useful.

Types

Models used for computer simulations can be grouped in different ways. One way is by whether they are based on chance or fixed rules, whether they show steady conditions or changes over time, and whether they deal with continuous or separate events.

Simulations can also be about how systems change in response to inputs, or they can try to find a balanced state. Some simulations use random numbers to model chance events, while others manage events in a timeline to test logic and design. Continuous simulations solve math equations to change the state of a system over time, used in areas like flight simulators and chemical modeling. Agent-based simulations represent individual entities directly, such as molecules or cells, with their own behaviors. Some simulations run across many connected computers, often called distributed simulations.

Visualization

In the past, the results from computer simulations were often shown in tables or grids. But people found it easier to understand trends by seeing graphs or moving images. For example, watching a weather map can help someone see that rain is coming their way much faster than reading numbers.

Today, weather forecasts often show moving clouds and maps with times to help us understand what will happen. Computer simulations can also show how something like a tumor might change over time in a medical scan, helping doctors see changes more clearly. These graphics help us see large amounts of data change during a simulation.

In science

Computer simulations help scientists study complex systems by using math models on computers. These models can represent things in nature, like how weather changes, how water flows, or how tiny particles behave.

Simulations can also model events that happen randomly, like changes in plant populations, or how materials react to force. They are used in many fields, from studying weather and water to understanding how our minds work and even designing new medicines. These tools help scientists predict what might happen in the real world and test ideas without needing to do expensive or difficult experiments.

In practical contexts

See also: List of geological modelling software

Computer simulations are used in many everyday situations. They help us study how air pollution spreads, design safe airplanes, and even create training tools for pilots through flight simulators. These simulations can also predict the weather, test new safety features in cars, and model how crops grow.

Engineers use simulations to test ideas without building real versions first, saving time and money. Animations created from these simulations can show us things like how people might move during an emergency. These tools help us understand complex systems, from traffic patterns to electrical circuits, in a safe and controlled way.

Pitfalls

When using computer simulations, it’s important to check how accurate the results are. This is done by testing how changes in important numbers affect the outcome. For example, when studying the chances of success in finding oil, scientists use many different possible values and a special method called the Monte Carlo method. If one of the main numbers, like how much rock holds oil, is only known roughly, the simulation’s result may not be very precise, even if it looks very exact.