19768802

Types Computer models can be classified according to several independent pairs of attributes, including: * Stochastic or deterministic (and as a special case of deterministic, chaotic) - see External links below for examples of stochastic vs. deterministic simulations * Steady-state or dynamic * Continuous or discrete (and as an important special case of discrete, discrete event or DE models) * Local or distributed. Equations define the relationships between elements of the modeled system and attempt to find a state in which the system is in equilibrium. Such models are often used in simulating physical systems, as a simpler modeling case before dynamic simulation is attempted. * Dynamic simulations model changes in a system in response to (usually changing) input signals. * Stochastic models use random number generators to model chance or random events; * A discrete event simulation (DES) manages events in time. Most computer, logic-test and fault-tree simulations are of this type. In this type of simulation, the simulator maintains a queue of events sorted by the simulated time they should occur. The simulator reads the queue and triggers new events as each event is processed. It is not important to execute the simulation in real time. It's often more important to be able to access the data produced by the simulation, to discover logic defects in the design, or the sequence of events. * A continuous dynamic simulation performs numerical solution of differential-algebraic equations or differential equations (either partial or ordinary). Periodically, the simulation program solves all the equations, and uses the numbers to change the state and output of the simulation. Applications include flight simulators, construction and management simulation games, chemical process modeling, and simulations of electrical circuits. Originally, these kinds of simulations were actually implemented on analog computers, where the differential equations could be represented directly by various electrical components such as op-amps. By the late 1980s, however, most "analog" simulations were run on conventional digital computers that emulate the behavior of an analog computer. * A special type of discrete simulation which does not rely on a model with an underlying equation, but can nonetheless be represented formally, is agent-based simulation. In agent-based simulation, the individual entities (such as molecules, cells, trees or consumers) in the model are represented directly (rather than by their density or concentration) and possess an internal state and set of behaviors or rules which determine how the agent's state is updated from one time-step to the next. * Distributed models run on a network of interconnected computers, possibly through the Internet. Simulations dispersed across multiple host computers like this are often referred to as "distributed simulations". There are several standards for distributed simulation, including Aggregate Level Simulation Protocol (ALSP), Distributed Interactive Simulation (DIS), the High Level Architecture (simulation) (HLA) and the Test and Training Enabling Architecture (TENA). source: http://en.wikipedia.org/wiki/Computer_model#Types

A mathematical model is the use of mathematical language to describe the behaviour of a system. A mathematical model usually describes a system by a set of variables and a set of equations that establish relationships between the variables. The variables represent some properties of the system, for example, measured system outputs often in the form of signals, timing data, counters, event occurrence (yes/no). The actual model is the set of functions that describe the relations between the different variables. [source: WordIQ online dictionary]

simulation models

computational models

models

computer-simulation models

simulation model

mathematical models

mathematical model

computational model

computer-simulation model

analytical classical model

Simulation model

agent-based spatially explicit simulation model

simulated patients

bead model

remodeling

animal models

coarse-grained (CG) model

ten Tusscher

multiprobe freezing model

CG AuNPs model

FitzHugh-Nagumo

live porcine model

Carnegie Ames Stanford Approach

3D reconstruction model

traditional clinical model

CellML models

coarse-grained (CG) models

in vivo model

DK-QN model

realistic physiological multiscale model

Compound Symmetry covariance model

restricted primitive model

multiscale modeling

Pattern-Oriented Modelling

animal model

multiscale model

multiscale models

DK-model

Hinge

TT04

comparative modeling

GLOPEM-CEVSA model

modeled

AR models

simulation model of Aedes aegypti populations

theoretic models

staggered timing model

CASA

atomic models

GLOPEM-CEVSA models

GastroPlus modeling

manikins

voxelized models

anatomic simulators

protein model

complex models

polymer models

many-state model

primitive chain network model

coarse-grained models

decision-analytic computer-simulation model

PLA

vitamin D forced model

remodelling

coarse-grained model

FHN

polymer model

protein models

lake specific models

Pattern-Oriented Model

bead models

perception-like situation models

model system

remodelling processes

Mathematical modelling

discretized models

Point-light actors

individual-based model

predictive model

slab model

trabecular bone remodeling

Computational modeling

rat migraine model

PLAs

CASA model

atomic model

manikin

solvent primitive model

Pattern-Oriented Models

hybrid programming models

current theoretical models

model complex

ten Tusscher (TT04) models

CG model

3D MIKE SHE groundwater resource model

mathematical modeling

field model

Modeling E.coli fate and transport

topology-based coarse-grained modeling