"Computer simulation is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of, or the outcome of, a real-world or physical system."
The creation of models that represent the system and its behavior, and simulation to verify and test the system's design.
Systems thinking: A framework for understanding complex systems, their components, and interactions.
Model types: Different model types and their applications. For instance, discrete event models and continuous models.
Simulation: Methods for simulating systems, including Monte Carlo and Discrete event simulation.
System dynamics: An approach to modeling complex systems based on feedback loops and causal relationships.
Optimization: Techniques for maximizing or minimizing system objectives, such as cost, response time, or efficiency.
Data analysis: Methods for interpreting data, including regression analysis, clustering, and time series analysis.
Probability and Statistics: Fundamental concepts of probability like distribution and the importance of data visualization along with data analysis.
Decision analysis: Approaches to analyzing decision-making when uncertainty or risk is involved.
Experimentation: Design and analysis of experiments intended to test different scenarios and compare their results.
Computer programming: Familiarity with programming languages like Python and R is crucial for implementing models into simulators.
Human factors: An important factor to be considered when designing models that will interact with human users.
Modeling languages and tools: Appreciation of modeling languages, such as Unified Modeling Language (UML) or Systems Modeling Language (SysML), and modeling tools like Simulink.
Verification and validation: Methods for testing the accuracy and validity of models and simulations.
Performance evaluation: Measurement of how well models and simulations are performing, including metrics and indicators.
Sensitivity analysis: Techniques for examining the impact of changing model inputs on its outputs.
Outcomes analysis: Analysis of the outcomes produced by models and simulations, including impact on stakeholders, unintended consequences or system dynamics.
Risk assessment: An assessment of the uncertainties and potential risks that can affect the system being modeled.
Optimization techniques: Techniques, such as Linear Programming or Dynamic Programming, for improving or optimizing outcomes.
Decision support systems: Technologies and tools that help users make more informed decisions based on evidence provided by models and simulations.
Applications of modeling and simulation: Examples of various fields, including transportation, the environment, social science, and finance, in which models and simulations are helpful.
System dynamics modeling: A type of modeling and simulation that is used to understand and describe the behavior of a complex system over time. In this type of modeling, a system is represented as a collection of interconnected feedback loops that are used to study the effect of changes in the system.
Agent-based modeling: A type of simulation that simulates the actions and interactions of autonomous agents within a system or environment to understand the behavior of the system.
Discrete-event simulation: A type of simulation that focuses on the simulation of the discrete events that occur in a system, such as customer arrivals or service completions.
Monte Carlo simulation: A type of simulation that uses random sampling to model and simulate the outcomes of a system or event over time. This type of simulation is often used in risk analysis and decision-making.
Finite element analysis (FEA): A type of simulation that focuses on the behavior of a system or structure under various loading conditions, such as stress or strain.
Computational fluid dynamics (CFD): A type of simulation that focuses on modeling and simulating the behavior of fluids and gases, such as the flow of air over an aircraft wing.
Optimization modeling: A type of modeling that focuses on finding the optimal solution to a problem, such as minimizing costs or maximizing profit.
Multi-objective optimization: A type of optimization modeling that considers multiple objectives and constraints simultaneously, such as balancing cost and quality.
Decision trees: A type of modeling that represents decisions and their potential consequences in a tree-like diagram, allowing for the analysis of different decision paths.
Game theory: A type of modeling that analyzes the strategic behavior of multiple decision makers in an environment, such as in business or politics.
"The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict."
"Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics), astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care, and engineering."
"Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions."
"Computer simulations are realized by running computer programs that can be either small, running almost instantly on small devices, or large-scale programs that run for hours or days on network-based groups of computers."
"The scale of events being simulated by computer simulations has far exceeded anything possible (or perhaps even imaginable) using traditional paper-and-pencil mathematical modeling."
"In 1997, a desert-battle simulation of one force invading another involved the modeling of 66,239 tanks, trucks, and other vehicles on simulated terrain around Kuwait, using multiple supercomputers in the DoD High Performance Computer Modernization Program."
"A 2.64-million-atom model of the complex protein-producing organelle of all living organisms, the ribosome, in 2005."
"A complete simulation of the life cycle of Mycoplasma genitalium in 2012."
"The Blue Brain project at EPFL (Switzerland), begun in May 2005 to create the first computer simulation of the entire human brain, right down to the molecular level."
"Because of the computational cost of simulation, computer experiments are used to perform inference such as uncertainty quantification."
"Computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics (computational physics)."
"Computer simulations have become a useful tool for the mathematical modeling of human systems in economics, psychology, social science, health care, and engineering."
"It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions."
"The reliability of some mathematical models can be determined by comparing their results to the real-world outcomes they aim to predict."
"The modeling of 66,239 tanks, trucks, and other vehicles on simulated terrain around Kuwait."
"A 2.64-million-atom model of the complex protein-producing organelle of all living organisms, the ribosome."
"To create the first computer simulation of the entire human brain, right down to the molecular level."
"Because of the computational cost of simulation, computer experiments are used to perform inference such as uncertainty quantification."
"Computer simulations have become a useful tool for the mathematical modeling of many natural systems in chemistry."