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 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, 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.
A hybrid system is a dynamical system that exhibits both continuous and discrete dynamic behavior – a system that can both flow and jump. Often, the term "hybrid dynamical system" is used, to distinguish over hybrid systems such as those that combine neural nets and fuzzy logic, or electrical and mechanical drivelines. A hybrid system has the benefit of encompassing a larger class of systems within its structure, allowing for more flexibility in modeling dynamic phenomena.
System of systems is a collection of task-oriented or dedicated systems that pool their resources and capabilities together to create a new, more complex system which offers more functionality and performance than simply the sum of the constituent systems. Currently, systems of systems is a critical research discipline for which frames of reference, thought processes, quantitative analysis, tools, and design methods are incomplete. The methodology for defining, abstracting, modeling, and analyzing system of systems problems is typically referred to as system of systems engineering.
An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.
In computer network research, network simulation is a technique whereby a software program replicates the behavior of a real network. This is achieved by calculating the interactions between the different network entities such as routers, switches, nodes, access points, links, etc. Most simulators use discrete event simulation in which the modeling of systems in which state variables change at discrete points in time. The behavior of the network and the various applications and services it supports can then be observed in a test lab; various attributes of the environment can also be modified in a controlled manner to assess how the network/protocols would behave under different conditions.
Scientific modelling is an activity that produces models representing empirical objects, phenomena, and physical processes, to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate. It requires selecting and identifying relevant aspects of a situation in the real world and then developing a model to replicate a system with those features. Different types of models may be used for different purposes, such as conceptual models to better understand, operational models to operationalize, mathematical models to quantify, computational models to simulate, and graphical models to visualize the subject.
A discrete-event simulation (DES) models the operation of a system as a (discrete) sequence of events in time. Each event occurs at a particular instant in time and marks a change of state in the system. Between consecutive events, no change in the system is assumed to occur; thus the simulation time can directly jump to the occurrence time of the next event, which is called next-event time progression.
PROMELA is a verification modeling language introduced by Gerard J. Holzmann. The language allows for the dynamic creation of concurrent processes to model, for example, distributed systems. In PROMELA models, communication via message channels can be defined to be synchronous, or asynchronous. PROMELA models can be analyzed with the SPIN model checker, to verify that the modeled system produces the desired behavior. An implementation verified with Isabelle/HOL is also available, as part of the Computer Aided Verification of Automata (CAVA) project. Files written in Promela traditionally have a .pml file extension.
PowerDEVS [BK011] is a general purpose software tool for DEVS modeling and simulation oriented to the simulation of hybrid systems. The environment allows defining atomic DEVS models in C++ language that can be then graphically coupled in hierarchical block diagrams to create more complex systems.
Model-based design (MBD) is a mathematical and visual method of addressing problems associated with designing complex control, signal processing and communication systems. It is used in many motion control, industrial equipment, aerospace, and automotive applications. Model-based design is a methodology applied in designing embedded software.
DEVS abbreviating Discrete Event System Specification is a modular and hierarchical formalism for modeling and analyzing general systems that can be discrete event systems which might be described by state transition tables, and continuous state systems which might be described by differential equations, and hybrid continuous state and discrete event systems. DEVS is a timed event system.
A segment of a system variable in computing shows a homogenous status of system dynamics over a time period. Here, a homogenous status of a variable is a state which can be described by a set of coefficients of a formula. For example, of homogenous statuses, we can bring status of constant and linear. Mathematically, a segment is a function mapping from a set of times which can be defined by a real interval, to the set [Zeigler76],[ZPK00], [Hwang13]. A trajectory of a system variable is a sequence of segments concatenated. We call a trajectory constant if its concatenating segments are constant.
Systems modeling or system modeling is the interdisciplinary study of the use of models to conceptualize and construct systems in business and IT development.
In mathematical modeling, deterministic simulations contain no random variables and no degree of randomness, and consist mostly of equations, for example difference equations. These simulations have known inputs and they result in a unique set of outputs. Contrast stochastic (probability) simulation, which includes random variables.
Tuncer Ören is Turkish Canadian systems engineer, professor emeritus of Computer Science at the School of Electrical Engineering and Computer Science (EECS) of the University of Ottawa, Canada, and Director, The McLeod Modeling and Simulation Network (M&SNet) of the SCS. He is known for his contributions to the methodology of modelling and simulation.
Bernard P. Zeigler is a Canadian engineer, and emeritus professor at the University of Arizona, known for inventing Discrete Event System Specification (DEVS) in 1976.
The Improved Performance Research Integration Tool (IMPRINT) is a discrete-event simulation and human performance modeling software tool developed by the Army Research Laboratory and Micro Analysis and Design. It is developed using the .NET Framework. IMPRINT allows users to create discrete-event simulations as visual task networks with logic defined using the C# programming language. IMPRINT is primarily used by the United States Department of Defense to simulate the cognitive workload of its personnel when interacting with new and existing technology to determine manpower requirements and evaluate human performance.
Micro Saint Sharp is a general purpose discrete-event simulation and human performance modeling software tool developed by Alion Science and Technology. It is developed using C# and the .NET Framework. Micro Saint Sharp allows users to create discrete-event simulations as visual task networks with logic defined using the C# programming language.
Gregory Zacharewicz is Full Professor at École des mines d'Alès in Alès, France. He recently joined the LGI2P laboratory in 2018 to develop research based on simulation. He was previously Associate Professor at the University of Bordeaux (2007-2018), where he conducted research on modeling, interoperability and simulation of business and social organization. He worked with Bruno Vallespir and Guy Doumeingts and under the direction of Claudia Frydman and Norbert Giambiasi.
Gabriel A. Wainer is a Canadian/Argentinian computer scientist known for his work in modeling and simulation. He is a Professor in the Department of Systems and Computer Engineering at Carleton University in Ottawa, Canada.
