Computer Simulations in Science
Updated 2026-09-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Computer simulations first entered meteorology and nuclear physics research after the Second World War, and have since become ubiquitous across the natural sciences, social sciences, and engineering, and even indispensable in some fields. They extend a theory's model by numerically evolving its equations when no analytical solution is available, and they raise new questions about verification and validation.
SCAFFOLDING EFFECT
Reduce cognitive load
- Analytical substitute: evolve the model fully when the equations have no analytical solution. - Error tracing: trace errors along the three links of equations, discretization, and code. - Experimental supplement: replace real experiments that cannot be carried out with parameter sweeps.
Anchor fast decisions
A simulation has epistemic value because it can carry a theoretical model through to the end when no analytical solution is obtainable: given the equations and initial conditions, numerical methods compute step by step how the system will evolve. Its reliability therefore depends on every link in the chain, whether the equations are right, whether discretization error is controlled, and whether the code implements them correctly.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- plato.stanford.eduhttps://plato.stanford.edu/entries/simulations-science/verified
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