Continuous simulation

Continuous Simulation refers to a computer model of a physical system that continuously tracks system response according to a set of equations typically involving differential equations.

It is notable as one of the first uses ever put to computers, dating back to the Eniac in 1946. Continuous simulation allows prediction of

Established in 1952, the Society for Modeling and Simulation International (SCS) is a nonprofit, volunteer-driven corporation dedicated to advancing the use of modeling & simulation to solve real-world problems. Their first publication strongly suggested that the Navy was wasting a lot of money through the inconclusive flight-testing of missiles, but that the Simulation Council's analog computer could provide better information through the simulation of flights. Since that time continuous simulation has been proven invaluable in military and private endeavors with complex systems. No Apollo moon shot would have been possible without it.

Continuous simulation must be clearly differentiated from discrete and discrete event simulation. Discrete simulation relies upon countable phenomena like the number of individuals in a group, the number of darts thrown, or the number of nodes in a Directed graph. Discrete event simulation produces a system which changes its behaviour only in response to specific events and typically models changes to a system resulting from a finite number of events distributed over time. A continuous simulation applies a Continuous function using Real numbers to represent a continuously changing system. For example, Newton's Second law of motion Newton's laws of motion, F = ma, is a continuous equation. A value, F (force), may be calculated exactly for any real number values of m (mass) and a (acceleration). The number of combinations of force and acceleration are infinite and therefore not discrete (countable).

Discrete simulations may be applied to represent continuous phenomena, but the resulting simulations produce approximate results. Continuous simulations may be applied to represent discrete phenomena, but the resulting simulations produce extraneous or impossible results for some cases. For example, using a continuous simulation to model a live population of animals may produce the impossible result of 1/3 of a live animal .

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