"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 process of creating a computer model of a real-world system and running experiments to analyze its behavior and performance.
Types of Simulations: Descriptions of Monte Carlo, Discrete-event, and Continuous Simulation, including their differences and applications.
Simulation Languages: Introduction to widely used simulation tools, such as ARENA, AnyLogic, and Simulink.
Probability Theory: Basic probability concepts, such as probability distributions, expected values, variance, and standard deviation.
Statistical Analysis: Describing how simulations use statistical analysis tools such as regression analysis, hypothesis testing, and ANOVA.
Input Modeling: Explaining how input distribution can be estimated from data and how to fit probability distributions to data.
Output Analysis: Exploring how to interpret simulation output and how to measure simulation performance.
Modeling Queuing Systems: Demonstrate queuing system models and describe the tools needed to work with such models.
System Dynamics Modeling: Introduction to System Dynamics modeling and simulation methodology.
Decision Making: Methods to use simulation to assist decision making while maximizing overall effectiveness.
Optimization: Examination of techniques behind optimization and the ways that is being used in Simulation.
Simulation Platforms: Description of a range of aspects related to Simulations Platforms, including Multi-agent based simulations, cloud simulations, and GIS-based simulations.
Case Studies: Examples and insights into Simulation techniques and processes with real-world situations from many different industries (e.g. healthcare, logistics, manufacturing).
Monte Carlo Simulation: A statistical technique used to examine the impact of different variables on an outcome by generating thousands of random simulations.
Agent-Based Modeling: A methodology where the behavior of individual agents is studied to understand the behavior of the system as a whole.
System Dynamics Simulation: A modeling approach that focuses on feedback loops and cause-and-effect relationships to understand how changes in one variable may affect the system in the long run.
Discrete Event Simulation: A technique that models systems that change state and events that cause transitions from one state to another to improve operational efficiency and assess risk.
Process Simulation: A method used to analyze and optimize manufacturing or service processes by creating a virtual model of the system.
Gaming Simulation: A type of simulation used to train or test decision-makers in a safe and controlled environment where they can experience the consequences of their decisions.
Human-in-the-Loop Simulation: A type of simulation where humans and machines interact in real-time to test and optimize complex systems.
Virtual Reality Simulation: A simulation technique that uses computer-generated environments to immerse individuals in a realistic and interactive experience.
Continuous Simulation: A type of simulation that models systems that evolve continuously over time, such as physical systems, chemical processes, or fluid dynamics.
Enterprise Simulation: A methodology that aims to simulate the entire organization, including its business processes, workflows, and resources, to optimize operations and improve decision-making.
"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."