Simulation and Emulation
Computer simulation is a numerical method for conducting computer-based experiments with mathematical models describing the behavior of a real object over a period. The approach is applicable in various fields, including virtual reality research (Nassar & Tekian, 2020), the social sphere (Huttar & BrintzenhofeSzoc, 2020), learning (Ben Ouahi et al., 2021), etc. The simulation model is a computer program designed for iterative execution by solving a dynamic system for the behavior of a given object, having developed a certain algorithm. The created model simulates real situations (with a certain scale and approximation) to study the behavior of the real object under the influence of external factors. In this reason, the simulation model must register the occurrence of certain events in time and accumulate the necessary data for conducting the analysis. In its operation, the simulator works with a model of the input flow of requests. When conducting a computer simulation, the created program (program simulation model) is executed in a computer environment, starting with the assignment of initial values for basic parameters (definition of initial conditions). During the execution of the program, values for the observed parameters are calculated, and at the end, corresponding estimates are formed. Typically, sequentially calculated scores are stored in a suitable manner so that they can be interpreted or analyzed together at the end. The main difference of computer simulation from other types of models (for example, analytical models) is that in it the corresponding program is executed in time, i.e., time is an important and sometimes critical factor.
Emulation is a special type of simulation to complement the functionality of a given system. An emulator is a program that replaces a missing hardware node and program code in the operation of a computer system. Typical examples of emulators are programs for supplementing system commands when ensuring compatibility between different versions of the operating system, emulating an arithmetic coprocessor, etc. The main feature of the emulating program is that it processes incoming real input data or programs, i.e., works with a real workload.
The benefit of emulation is that it supports scientific research using computer experiments based on computer models. In these, it is possible to change the input effects, mostly by modulating the input stream, to examine the output results. Flows with Gaussian intensity are one of the popular tools in computational means because it allows interpolation of input settings (Katzfuss et al., 2022). The paper argues that their use on large datasets does not lead to useful results, and therefore proposes an extended and powerful model for large-scale analysis and emulation of computer experiments. The emulation uses an ordered conditional approximation when transforming the input space, for which parameters in the covariance function of the Gaussian process are pre-estimated. The result is the ability to obtain an estimate of joint prediction and simulation in nearly linear time in the number of model’s runs.
Emulation as an approach is applied in various studies where real input workflow data is available. For example, in (Gregor et al., 2022) emulation was used in a model investigation of the processes in a business logistics system, with the possibility of flexible response to possible changes. The emulation is implemented in a mixed real-world and virtual-world environment to verify the functionality of the entire process in route optimization of automated guided vehicles. The model proposed in the article was developed in the Tecnomatix Plant Simulation software environment, and the results provided estimates for routing capacity research in internal logistics processes. In general, the achieved effect is a shortening of the time for preliminary design and optimization of operations, including the time for testing the logistics system. The technological aspect of application of emulation is also reflected in (Ram et al., 2018) in the analysis of energy resources and the search for alternative sources of energy. Solar energy is one of the leading sources of “new” energy, allowing it to replace traditional ones, but the real-time study of photovoltaic systems is a difficult task. The reason is that such experiments require an accurate emulator to reproduce the nonlinear characteristics of the photovoltaic cell. In order to offer researchers, the opportunity to make an adequate choice, the article reviews various emulators, uniting them under general indicators of comparability, for example, costs and accuracy of emulation, complexity of development, level of sensitivity to external influences, etc.
Quantum technologies are new to the computing field, with standard primitives of quantum computing involving deterministic unitary gates distinct from traditional operations (Bartolucci et al., 2023). The article presents a model for fault-tolerant quantum computing built from physical primitives. It is stated that the model offers direct error handling from the quantum correction protocol, and the proposed architecture has a modular organization and reduced requirements compared to other quantum architectures. Another paper developed a class of emulators to study the quantum scattering problem (Zhang & Furnstahl, 2022). A combination of the variational method of scattering the observable parameters and the concept of eigenvector continuation is used. The emulator is pre-trained by a flow Hamiltonian for a small number of points in space, and as a next step, the necessary interpolations and extrapolations are performed in that space. Emulation computation time estimates are on the millisecond scale with highly minimized errors and low memory usage.
Computer simulation is a reliable approach to studying systems and processes if it provides sufficiently correct results in the relevant domain. It also depends on the correctness of the initial conditions and the set values for the input controllable parameters. It is desirable that the simulation gives very accurate results, but in certain cases a certain tolerance and approximation of the estimates is permissible. However, the accuracy of the analysis also depends on the task at hand and the size of the research area – the wider the research domain, the more difficult it is to obtain precise simulation results.
Computer simulation is a suitable approach for modeling systems in which random (probabilistic) processes develop, as well as in situations such as the following.
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Inaccessible research object, impossible or very expensive research in real conditions of a system or a real process.
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Tasks allowing the formulation of an acceptable analytical or functional model, which, however, would be difficult to solve with known mathematical means or due to peculiarities of the used modeling apparatus, the necessary solution cannot be found.
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Serious difficulties or impossibility to correctly validate an established mathematical model of a system or process due to unclear operating conditions or insufficient behavioral data.