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
- 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
- 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
- Natural language processing
- 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.
- 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.