Modeling and Simulation

Modeling & Simulation

Modeling and simulation (M&S) is the use of 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 supports analysis, experimentation, and training. As such, M&S can facilitate understanding a system's behavior without actually testing the system in the real world.

The big ideas

The Basic Model

• Computer modeling
• Systems that can be modelled
• Variables required to model a given system
• Limitations of computer (mathematical) models
• Sensible grouping for collections of data items
• Test-cases to evaluate a model
• Effectiveness of test-cases in a specified situation
• Correctness of a model

Simulations

• Define the term simulation
• Model and a simulation
• Rules of simulations
• Rules and data representations
• Simple models
• Test-cases to evaluate simulations
• Software and hardware required for a simulation
• Changes in rules, formulae and algorithms
• Examples of simulations that involve changes in rules, formulae and algorithms
• Changes in data collection that could improve the model or simulation
• Reliability of a simulation
• Advantages and disadvantages of simulation
• Advantages and disadvantages of using a simulation for making predictions

Visualizations

• Define the term visualization
• Two-dimensional use of visualization
• Memory needs of 2D visualization
• Three-dimensional use of visualization
• Images in memory and the 3D visualization
• Time and memory considerations of 3D animation

Communication modeling and simulation (HL only)

• Genetic algorithms
• Structure of neural networks
• Neural network modeling
• Neural networks, recognizing patterns
• Compare different ways in which neural networks can be used to recognize patterns
• Key structures of natural language
• Human and machine learning when related to language
• Evolution of modern machine translators
• Chatbots
• Natural language processing

helpful resources

• https://sim4edu.com/

Standards

• Define the term computer modelling.
• Identify a system that can be modelled.
• Identify the variables required to model a given system.
• Describe the limitations of computer (mathematical) models.
• Outline sensible grouping for collections of data items, including sample data.
• Design test-cases to evaluate a model.
• Discuss the effectiveness of a test-case in a specified situation.
• Discuss the correctness of a model by comparing generated results with data that were observed in the original problem.
• Define the term simulation.
• Explain the difference between a model and a simulation.
• Describe rules that process data appropriately and that produce results.
• Discuss rules and data representations and organization.
• Construct simple models that use different forms of data representation and organization.
• Design test-cases to evaluate a simulation program.
• Outline the software and hardware required for a simulation.
• Describe changes in rules, formulae and algorithms that would improve the correspondence between results and observed data.
• Construct examples of simulations that involve changes in rules, formulae and algorithms.
• Describe changes in data collection that could improve the model or simulation.
• Discuss the reliability of a simulation by comparing generated results with data that were observed in the original problem.
• Outline the advantages and disadvantages of simulation in a given situation rather than simply observing a real-life situation.
• Discuss advantages and disadvantages of using a simulation for making predictions.
• Define the term visualization.
• Identify a two-dimensional use of visualization.
• Outline the memory needs of 2D visualization
• Identify a three-dimensional use of visualization.
• Outline the relationship between the images in memory and the 3D visualization.
• Discuss the time and memory considerations of 3D animation in a given scenario.

HL standards

• Outline the use of genetic algorithms.
• Outline the structure of neural networks.
• Compare applications that use neural network modelling.
• Compare different ways in which neural networks can be used to recognize patterns.
• Identify the key structures of natural language.
• Discuss the differences between human and machine learning when related to language.
• Outline the evolution of modern machine translators.
• Describe the role of chatbots to simulate conversation.
• Discuss the latest advances in natural language processing.

Give the precise meaning of a word, phrase, concept or physical quantity.

Provide an answer from a number of possibilities. Recognize and state briefly a distinguishing fact or feature.

Give a detailed account or picture of a situation, event, pattern or process.

Give a brief account.

Produce a plan, simulation or model.

Assess the implications and limitations; make judgments about the ideas, works, solutions or methods in relation to selected criteria.

Offer a considered and balanced review that includes a range of arguments, factors or hypotheses. Opinions or conclusions should be presented clearly and supported by appropriate evidence.

Give a detailed account including reasons or causes.

Develop information in a diagrammatic or logical form.

a group or system of interconnected people or things.

a group or system of interconnected people or things.