Modeling and simulation in engineering

• Published in: Wiley interdisciplinary reviews. Computational statistics 2013-05, Vol.5 (3), p.239-266
• Main Author: Sahinoglu, Mehmet
• Format: Article
• Language: English
• Subjects: Astronomical models; Astronomy; Bayesian; Computer applications; Computer simulation; Computers; Conflicts of interest; cyber-security; Cybersecurity; Data analysis; Data processing; Digital computers; Disasters; discrete event/Monte Carlo; Engineering; Graphical methods; Higgs bosons; Innovations; Machine learning; Manufacturing industry; Markov analysis; Markov chains; Mathematical models; modeling; Modelling; Monte Carlo simulation; multistate; New products; Precautions; Product innovation; production; Program verification (computers); Random numbers; Simulation; Software; Solutions; Statistical analysis; Statistical methods; Statistical models; Statistics; Universe; Websites; Wire; Zoology
• ISSN: 1939-5108; 1939-0068
• DOI: 10.1002/wics.1254

This review article will explore the innovative and popular theme of engineering modeling and simulation, predominantly in the manufacturing industry and cybersecurity world, citing severe challenges, advantages and time‐ and budget saving solutions and its future. The power of simulation is not an exaggeration but an understatement. The favorable outcomes since the advent of digital computers and software revolution could not have been achieved, especially without the multiple benefits of statistical simulation, which underlies the widespread use of modeling and simulation in engineering and sciences, stretching from A (Astronomy) to Z (Zoology). This refers not only to research findings in verifying a certain piece of theory, such as that of the recently discovered Higgs Boson, but in testing new products to innovate new discoveries so as to make our universe a more peaceful place by modeling and simulating the future projects and taking precautions before disasters occur. The review explores a cross section of engineering modeling and simulation practices illustrating a window of numerical examples. WIREs Comput Stat 2013, 5:239–266. doi: 10.1002/wics.1254 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC) Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Algorithms and Computational Methods > Random Number Generation Statistical Models > Simulation Models