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Modelling and simulation

Simulation always have a model and modeling is a part of simulation. To emphasize the modeling involved in simulation, simulation is commonly referred to as Modeling and Simulation (M&S).
Simulation is the act of executing, experimenting with or exercising a model or a set of models for a specific goal such as:-

  • Problem Solving
  • Training
  • Acquisition
  • Entertainment
  • Research
  • Education

Modelling and Simulation is a large and diverse discipline used for providing solutions to complex problems encountered in almost every field such as:-

  • engineering
  • business
  • sciences (e.g., agricultural, biological, medical, social)
  • military
  • government


Intended Modelling and simulation objectives is Evalution,prediction,comparison,sensitive analysis,optimisation,ranking and selection,etc.


Types of various modelling and simulation are:-

  • Discreate M&S
  • Continuous M&S
  • Monte Carlo M&S
  • System Dynamics M&S
  • Gaming-based M&S
  • Agent-based M&S
  • AI-based simulation model
  • Virtual Reality-based M&S and so on.

Modeling and simulation (M&S) refers to using models – physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process – as a basis for simulations – methods for implementing a model (either statically or) over time – to develop data as a basis for managerial or technical decision making. M&S helps getting information about how something will behave without actually testing it in real life. For instance, to determine which type of spoiler would improve traction the most while designing a race car, a computer simulation of the car could be used to estimate the effect of different spoiler shapes on the coefficient of friction in a turn. Useful insights about different decisions in the design could be gleaned without actually building the car.
The use of M&S within engineering is well recognized. Simulation technology belongs to the tool set of engineers of all application domains and has been included in the body of knowledge of engineering management. M&S helps to reduce costs, increase the quality of products and systems, and document and archive lessons learned.
M&S is a discipline on its own. Its many application domains often lead to the assumption that M&S is pure application. This is not the case and needs to be recognized by engineering management experts who want to use M&S.
The major courses to study in modeling and simulation are Modeling, Modeling Concepts and Definitions, MATLAB Scripts, MATLAB Arrays, Linear models, Graphing data in MATLAB, MATLAB Array Math, Nonlinear Functions, Curve fitting, MATLAB I/O, Stochastic models etc.

Some of the homework help topics include :

  • Terminology & Components of modelling & simulation,Discrete vs. Continuous time,Process flow in simulation study,Queuing systems,Communications networks,Event-driven simulation,World Views & List processing,Simulation in High Level Language ,Simulation packages
  • Interpreted vs. compiled simulators,Statistical models,Queuing models: Steady-State Behavior,Networks of Queues,Properties of Random Numbers,Generation of Pseudo-Random Numbers,Testing for Randomness,Pitfalls;Random Variate Generation:Inverse & Direct Transform,Convolution,Accept-Reject,Collecting Data;
  • Identifying Distribution,Histograms,Parameter Estimation,Goodness-of-Fit,Selecting Input Model without Data,Verification and Validation of Simulation Models,Calibration and Validation of Models,Types of Simulations with Respect to Output Analysis

Modelling and simulation Assignment as a Service questions help services by live experts :

  • 24/7 Chat, Phone & Email support
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  • Live help for Modelling and simulation Assignment online quiz & online tests

Help for complex topics like :

  • Stochastic Nature of Output Data,Measures of Performance,Output Analysis for Termination Simulations,Output Analysis for Steady-State Simulations,,System analysis,conceptual, abstract, and simulation models,Multimodels,Simulation systems and languages,basic means of model and experiment description,simulation system implementation,Generating, transformation
  • Testing of pseudo random numbers,Monte Carlo methods,Parallel process modelling,Petri nets in simulation,Discrete simulation model,Time and simulation experiment control,Next-event algorithm,Continuous systems modelling,numerical methods used for continuous simulation
  • Dymola simulation system.,Combined/hybrid simulation,Modelling of digital systems,models of heterogeneous systems,Model optimization.,Analytical solution of queuing system models,Cellular automata and simulation,Checking of model validity ,Verification of models,Analysis & Visualization of simulation results 

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