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Libros sobre el tema "Simulation"

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Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 20 de julio de 2024

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1

H, Abou-Kassem Jamal, ed. Reservoir simulations handbook: Understanding reservoir simulation development. Houston, TX: Gulf Pub. Co., 2006.

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2

Johnston, Lindsay C. y Lillian Su, eds. Comprehensive Healthcare Simulation: ECMO Simulation. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53844-6.

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3

Gleiniger, Andrea y Georg Vrachliotis, eds. Simulation. Berlin, Boston: DE GRUYTER, 2008. http://dx.doi.org/10.1515/9783034610353.

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4

Lenhard, Johannes, Günter Küppers y Terry Shinn, eds. Simulation. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-5375-4.

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5

Robinson, Stewart. Simulation. London: Macmillan Education UK, 2014. http://dx.doi.org/10.1007/978-1-137-32803-8.

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6

Thompson, James R., ed. Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1999. http://dx.doi.org/10.1002/9780470317068.

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7

Ross, Sheldon M. Simulation. 4a ed. Amsterdam: Elsevier Academic Press, 2006.

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8

Ross, Sheldon M. Simulation. 3a ed. San Diego: Academic Press, 2002.

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9

Lucot, Hubert. Simulation. [Paris]: Impr. nationale, 1990.

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10

Ross, Sheldon M. Simulation. 2a ed. San Diego: Academic Press, 1997.

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11

G, Henderson Shane y Nelson Barry L, eds. Simulation. Amsterdam: Elsevier, 2006.

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12

Successful simulation: A practical approach to simulation projects. London: McGraw-Hill, 1994.

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13

Simulating war: Studying conflict through simulation games. London: Continuum, 2012.

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14

Carstens, Patricia K., Paul Paulman, Audrey Paulman, Marissa J. Stanton, Brian M. Monaghan y Douglas Dekker, eds. Comprehensive Healthcare Simulation: Mobile Medical Simulation. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33660-8.

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15

Simulating security returns: A filtered historical simulation approach. New York City, NY: Palgrave Pivot, 2014.

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16

Nigel, Gilbert G. y Doran Jim, eds. Simulating societies: The computer simulation of social phenomena. London: UCL Press, 1994.

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17

Bangsow, Steffen. Manufacturing Simulation with Plant Simulation and SimTalk. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05074-9.

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18

Saleh, Resve, Shyh-Jye Jou y A. Richard Newton. Mixed-Mode Simulation and Analog Multilevel Simulation. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-5854-2.

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19

Cummings, Gerard F. GPSS/PC simulation tutorials: General purpose simulation. Stow,Mass: MINUTEMAN Software, 1986.

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20

Saleh, Resve A. Mixed-mode simulation and analog multilevel simulation. Boston: Kluwer Academic, 1994.

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21

Saleh, Resve. Mixed-Mode Simulation and Analog Multilevel Simulation. Boston, MA: Springer US, 1994.

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22

Stochastic simulation. New York: Wiley, 1987.

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23

1934-, Zobrist George W. y Leonard James V, eds. Simulation systems. Amsterdam: Gordon & Breach, 2000.

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24

Surgical simulation. London: Anthem Press, 2013.

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25

Venus, Jochen. Referenzlose Simulation? Würzburg: Königshausen & Neumann, 1997.

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26

Najm, Farid N. Circuit simulation. Hoboken, N.J: John Wiley & Sons, 2010.

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27

Page, Bernd. Diskrete Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76862-0.

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28

Bello, Fernando y P. J. Eddie Edwards, eds. Biomedical Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-70521-5.

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29

Gantt, Laura T. y H. Michael Young, eds. Healthcare Simulation. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118949443.

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30

Zhang, Weihong y Min Wan. Milling Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119262893.

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31

Kolonko, Michael. Stochastische Simulation. Wiesbaden: Vieweg+Teubner, 2008. http://dx.doi.org/10.1007/978-3-8348-9290-4.

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32

Baum, Marcus, Gunther Brenner, Jens Grabowski, Thomas Hanschke, Stefan Hartmann y Anita Schöbel, eds. Simulation Science. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96271-9.

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33

Dede, Ercan M., Jaewook Lee y Tsuyoshi Nomura. Multiphysics Simulation. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5640-6.

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34

Topçu, Okan, Umut Durak, Halit Oğuztüzün y Levent Yilmaz. Distributed Simulation. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-03050-0.

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35

Lee, Alfred T. Vehicle Simulation. Boca Raton : Taylor & Francis, CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/b22297.

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36

Rouse, William B. y Kenneth R. Boff, eds. Organizational Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471739448.

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37

Rouse, William B. y Kenneth R. Boff, eds. Organizational Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471739448.

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38

O'Sullivan, David y George L. W. Perry. Spatial Simulation. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118527085.

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39

Lenhard, Johannes. Computer Simulation. Editado por Paul Humphreys. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199368815.013.42.

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This article interprets computer simulation modeling as a new type of mathematical modeling that comprises a number of interdependent components, among them experimentation, visualization, and adaptability. Furthermore, it is argued, simulation modeling can be characterized as a particular style of reasoning, namely a combinatorial style, that assembles and balances elements from different other styles. Two examples are discussed that exemplify the transformative force of this style: what counts as “understanding phenomena” and what counts as a “solution.” Both are seminal pieces of traditional mathematical modeling and both are transformed, if not inverted, in simulation modeling. Finally, some challenges are considered that computer simulations pose for philosophy of science.
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40

Vogt, William G. Modeling & Simulation (Modeling & Simulation). ISA International Society for Measurement and, 1990.

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41

Allen, Michael P. y Dominic J. Tildesley. Computer Simulation of Liquids. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.001.0001.

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This book provides a practical guide to molecular dynamics and Monte Carlo simulation techniques used in the modelling of simple and complex liquids. Computer simulation is an essential tool in studying the chemistry and physics of condensed matter, complementing and reinforcing both experiment and theory. Simulations provide detailed information about structure and dynamics, essential to understand the many fluid systems that play a key role in our daily lives: polymers, gels, colloidal suspensions, liquid crystals, biological membranes, and glasses. The second edition of this pioneering book aims to explain how simulation programs work, how to use them, and how to interpret the results, with examples of the latest research in this rapidly evolving field. Accompanying programs in Fortran and Python provide practical, hands-on, illustrations of the ideas in the text.
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42

Baker, Pat. Simulation Games 3 (Simulation Games). Morehouse Group, 1986.

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43

Baker, Pat y Mary-Ruth Marshall. Simulation Games 2 (Simulation Games). Morehouse Group, 1986.

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44

(Illustrator), Michael Linndell, ed. Simulation Games 1 (Simulation Games). 3a ed. Morehouse Group, 1986.

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45

Verma, Jyoti. Matlab Simulation Fundamental: Simulation Model. Independently Published, 2020.

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46

STANDRIDGE. Simulation. John Wiley & Sons Inc, 1998.

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47

Ross, Sheldon M. Simulation. Elsevier Science & Technology Books, 2002.

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48

Wigginton, Mark, Miguel Garcia, Timothy J. Draycott y Neil A. Muchatuta. Simulation. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198713333.003.0053.

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Simulation can be a powerful tool in obstetric anaesthesia, driving forwards the education of clinicians for the benefit of patients. Simulation has been closely linked with obstetrics since its inception. Today’s modern technology and educational theory have combined to make it a more powerful and accessible learning tool than ever, allowing both clinical skills and human factors to be effectively taught and assessed in combination. Since becoming more widely validated, simulation is also being used in research, to identify latent threats and for summative assessment. Setting up a simulation programme, whether in situ or at a dedicated centre, requires preparation, planning, and an appreciation of its limitations. The simulation should be evidence based, target the learner’s needs, and be of benefit to patients. The challenge for trainers and trainees is to ensure both that the training provided achieves these goals, and that they can deliver evidence to demonstrate that it has.
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49

Boudreau, Joseph F. y Eric S. Swanson. Simulation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198708636.003.0015.

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This chapter is devoted to Monte Carlo simulation of stochastic processes, both fundamental processes and those involving radiation transport through macroscopic material. The computation of fundamental processes builds on the treatment of rotations and Lorentz transformations from the previous chapter and expands it with a discussion of computational techniques for the evaluation of Feynman diagrams. The simulation of radiation transport covers electromagnetic processes such as ionization energy loss, bremsstrahlung, and pair production. A discussion of real-life challenges in the simulation of radiation transport is included, as well as a brief discussion of simulation toolkits that are available for solving industrial-strength problems. The discussion is intended to give an overview of some of the principal computational and numerical techniques enabling these toolkits.
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50

Ross, Sheldon M. Simulation. Elsevier Science & Technology Books, 2012.

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