Skip to main content
SpringerLink
Log in

Core Research Areas

  • Chapter
  • First Online:
Body of Knowledge for Modeling and Simulation

Part of the book series: Simulation Foundations, Methods and Applications ((SFMA))

  • 702 Accesses

Abstract

The SCS M&S Body of Knowledge is a living concept, and core research areas are among those that will drive its progress. In this chapter, conceptual modeling constitutes the first topic, followed by the quest for model reuse. As stand-alone applications become increasingly rare, embedded simulation is of particular interest. In the era of big data, data-driven M&S gains more interest as well. Applying the M&S Framework (MSF) to enable neuromorphic architectures exemplifies the ability of simulation to meaningfully contribute to other fields as well. The chapter closes with sections on model behavior generation and the growth of simulation-based disciplines.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fishwick P (1995) Simulation model design and execution. Prentice Hall, Building Digital Worlds

    Google Scholar 

  2. Gentner D, Stevens AL (2014) Mental models. Psychology Press

    Book  Google Scholar 

  3. Johnson-Laird P (1986) Mental models 1986. Harvard University Press.

    Google Scholar 

  4. Enderton H (2001) A mathematical introduction to logic. Academic Press

    MATH  Google Scholar 

  5. Knight K (2012) Mind mapping: improve memory, concentration, communication, organization, creativity, and time management. MindLily Publishing

    Google Scholar 

  6. Novak JD (2010) Learning, creating and using knowledge: concept maps as facilitative tools in schools and corporations. Routledge

    Book  Google Scholar 

  7. Tolk A, Turnitsa C, Diallo S (2008) Implied ontological representation within the levels of conceptual interoperability model. Intell Decis Technol 2(1):3–19

    Article  Google Scholar 

  8. Matthews RB, Gilbert NG, Roach A, Polhill JG, Gotts NM (2007) Agent-based land-use models: a review of applications. Landscape Ecol 22(10):1447–1459

    Article  Google Scholar 

  9. Tolk A, Diallo SY, Padilla JJ, Herencia-Zapana H (2013) Reference modelling in support of M&S—foundations and applications. J Simul 7(2):69–82

    Article  Google Scholar 

  10. Diallo SY, Wildman WJ, Shults FL, Tolk A (eds) (2019) Human simulation: perspectives, insights, and applications (vol 7). Springer, Cham

    Google Scholar 

  11. Robinson S, Arbez G, Birta LG, Tolk A, Wagner G (2015). Conceptual modeling: definition, purpose and benefits. Proceedings of the Winter Simulation Conference, IEEE: Piscataway, NJ. pp. 2812–2826

    Google Scholar 

  12. Robinson S, Nance RE, Paul RJ, Pidd M, Taylor SJ (2004) Simulation model reuse: definitions, benefits and obstacles. Simul Model Pract Theory 12(7–8):479–494

    Article  Google Scholar 

  13. MIL-HDBK 1211 (1995) Missile flight simulation part one surface-to-air missiles. U.S. Department of Defense

    Google Scholar 

  14. STANAG. 4355 (2003) The modified point mass and five degrees of freedom trajectory models, Draft Edition 5.0A

    Google Scholar 

  15. Pidd M (2002) Simulation software and model reuse: a polemic. In: Proceedings of the winter simulation conference, vol 1. IEEE, pp 772–775

    Google Scholar 

  16. Balci O, Arthur JD, Nance RE (2008) Accomplishing reuse with a simulation conceptual model. In: 2008 winter simulation conference. IEEE, pp 959–965

    Google Scholar 

  17. Reese R, Wyatt DL (1987) Software reuse and simulation. In: Proceedings of the 19th conference on winter simulation, pp 185–192

    Google Scholar 

  18. Zeigler BP, Hall SB, Sarjoughian HS (1999) Exploiting HLA and DEVS to promote interoperability and reuse in lockheed’s corporate environment. SIMULATION 73(5):288–295

    Article  Google Scholar 

  19. Hu Y, Xiao J, Zhao H, Rong G (2013) Devsmo: an ontology of devs model representation for model reuse. In: Proceedings of the 2013 winter simulation conference: simulation: making decisions in a complex world, pp 4002–4003

    Google Scholar 

  20. Garro A, Falcone A (2015) On the integration of HLA and FMI for supporting interoperability and reusability in distributed simulation. In: Proceedings of the symposium on theory of modeling & simulation: DEVS integrative M&S symposium, pp 9–16

    Google Scholar 

  21. Exel L, Frey G, Wolf G, Oppelt M (2014) Re-use of existing simulation models for DCS engineering via the Functional Mock-up Interface. In: Proceedings of the 2014 IEEE emerging technology and factory automation (ETFA). IEEE, pp 1–4

    Google Scholar 

  22. Bell D, de Cesare S, Lycett M, Mustafee N, Taylor SJ (2007) Semantic web service architecture for simulation model reuse. In: 11th IEEE international symposium on distributed simulation and real-time applications (DS-RT’07). IEEE, pp 129–136

    Google Scholar 

  23. Bocciarelli P, D’Ambrogio A, Giglio A, Paglia E (2019) A microservice-based approach for fine-grained simulation in msaas platforms. In: Proceedings of the 2019 summer simulation conference, pp 1–12.

    Google Scholar 

  24. Szabo C, Teo YM (2007) On syntactic composability and model reuse. In: First Asia international conference on modelling & simulation (AMS’07). IEEE, pp 230–237

    Google Scholar 

  25. Diallo SY, Herencia-Zapana H, Padilla JJ, Tolk A (2011) Understanding interoperability. In: Proceedings of the 2011 emerging M&S applications in industry and academia symposium, pp 84–91

    Google Scholar 

  26. Bell D, Mustafee N, de Cesare S, Taylor SJ, Lycett M, Fishwick PA (2008) Ontology engineering for simulation component reuse. Int J Enterp Inf Syst (IJEIS) 4(4):47–61

    Article  Google Scholar 

  27. Durak U, Oğuztüzün H, Köksal Algin C, Özdikiş Ö (2011) Towards interoperable and composable trajectory simulations: an ontology-based approach. J Simul 5(3):217–229

    Article  Google Scholar 

  28. Tolk A, Diallo S, Turnitsa C (2007) Applying the levels of conceptual interoperability model in support of integratability, interoperability, and composability for system-of-systems engineering. J Syst Cybern Inform 5(5):65–74

    Google Scholar 

  29. Handler (2019) Automatic innovation: ubiquitous simulation grid technology. Baidu

    Google Scholar 

  30. Hill R, Al-Muhtadi J, Campbell R, Kapadia A, Ranganathan A (2004) A middleware architecture for securing ubiquitous computing cyber infrastructures. IEEE Distrib Syst Online 5(9):1–1

    Article  Google Scholar 

  31. Li BH, Chai XD, Zhang L et al (2018) Preliminary study on modeling and simulation technologies for new artificial intelligent systems. J Syst Simul 30(2):349–362

    Google Scholar 

  32. Li BH (2005) Some focusing points in development of modern modeling and simulation technology. Lecture Notes Comput Sci 3398(9):12–22

    Google Scholar 

  33. Li N, Xu LJ, Peng XY et al (2008) Study on ubiquitous simulation system architecture and key technologies. J Syst Simul 16:131–135

    Google Scholar 

  34. Tang Z, Li BH, Chai XD (2008) Application of context-awareness in pervasive simulation grid. Comput Integr Manuf Syst 08:96–104

    Google Scholar 

  35. Tang Z, Li BH, Chai XD et al (2008) Study on ubiquitous simulation grid. In: Computer integrated manufacturing systems. 08

    Google Scholar 

  36. Tang Z (2007) Study on ubiquitous simulation grid and key technologies. Beihang University

    Google Scholar 

  37. Zhai Y, Sun W, Bao T, Yang K, Qing D (2018) Edge-side simulation method and framework based on micro-services. J Syst Simul 30(12):44–53

    Google Scholar 

  38. Sandhu R, Thomas RK (2004) Models, protocols, and architectures for secure pervasive computing: challenges and research directions. In: Proceedings of the second IEEE annual conference on pervasive computing and communications workshops. IEEE

    Google Scholar 

  39. Xu WS, Xin YW, Lu GZ (2007) Research and development of pervasive computing middleware technology. Comput Sci 34(11):1–5

    Google Scholar 

  40. Wu Q (2006) Research on model and methodology of adaptive middleware for ubiquitous computing. Zhejiang University

    Google Scholar 

  41. Tang Z, Li BH, Chai XD et al (2009) Studies on simulation service migration technologies in pervasive simulation grid. J Syst Simul 12:3631–3640

    Google Scholar 

  42. Maffioletti S, Kouadri MS, Hirsbrunner B (2004) Automatic resource and service management for ubiquitous computing environments. In: IEEE conference on pervasive computing & communications workshops. IEEE

    Google Scholar 

  43. Yau SS, Zhang X (2005) A middleware service for secure group communication in mobile ad hoc networks, vol 76. Elsevier Inc., pp 29–43

    Google Scholar 

  44. Zheng, D (2009) Research on key technologies of component oriented middleware based on the context-aware service. National University of Defense Technology

    Google Scholar 

  45. Banks J (1998) Handbook of simulation—Principles, methodology, advances, applications, and practice. Wiley

    Google Scholar 

  46. Sargent R.G. (2011). Verification and Validation of Simulation Models. Proc.of the 2011 Winter Simulation Conference. (S. Jain, R.R. Creasey, J. Himmelspach, K.P. White, and M. Fu, eds.), pp. 183–198.

    Google Scholar 

  47. Ciuffo B, Punzo V, Montanino M (2012) The calibration of traffic simulation models. Report on the assessment of different goodness of fit measures and optimization algorithms. MULTITUDE Project-COST Action TU0903. Technical report, European Commission-Joint Research Centre

    Google Scholar 

  48. Kesting A, Treiber M (2008) Calibrating car-following models by using trajectory data: methodological study. Transp Res Rec: J Transp Res Board 2088:148–156

    Article  Google Scholar 

  49. Huang Y (2013) Automated simulation model generation. Ph.D. thesis, Delft University of Technology

    Google Scholar 

  50. Zeigler BP, Praehofer H, Kim TG (2000) Theory of modeling and simulation: integrating discrete event and continuous complex dynamic systems, 2nd edn. Academic Press

    Google Scholar 

  51. Lahoz WA, Khattatov B, Menard R (2010) Data assimilation: making sense of observations, 1st edn. Springer, Berlin, Heidelberg

    Google Scholar 

  52. Darema F (2004) Dynamic data driven applications systems: A new paradigm for application simulations and measurements. In: Bubak M, van Albada GD, Sloot PMA, Dongarra J (eds) Computational science—ICCS 2004. Springer, Berlin, Heidelberg, pp 662–669

    Chapter  Google Scholar 

  53. Darema F (2005) Dynamic data driven applications systems: New capabilities for application simulations and measurements. In: Sunderam VS, van Albada GD, Sloot PMA, Dongarra JJ (eds) Computational Science—ICCS 2005. Springer, Berlin, Heidelberg, pp 610–615

    Chapter  Google Scholar 

  54. Hu X (2011) Dynamic data driven simulation. SCS M&S Mag II(1):16–22

    Google Scholar 

  55. Bouttier F, Courtier P (1999) Data assimilation concepts and methods. Meteorological Training Course Lecture Series, ECMWF (European Centre for Medium-Range Weather Forecasts)

    Google Scholar 

  56. Nichols NK (2003) Data assimilation: aims and basic concepts. Springer, Netherlands, Dordrecht, pp 9–20

    Google Scholar 

  57. Bai F, Guo S, Hu X (2011) Towards parameter estimation in wildfire spread simulation based on sequential Monte Carlo methods. In: Proceedings of the 44th annual simulation symposium, Boston, MA, USA, pp 159–166

    Google Scholar 

  58. Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188

    Article  Google Scholar 

  59. Gillijns S, Mendoza O, Chandrasekar J, De Moor BLR, Bernstein D, Ridley A (2006) What is the ensemble Kalman filter and how well does it work? In: Proceedings of the 2006 American control conference, Minneapolis, MN, USA, pp 4448–4453

    Google Scholar 

  60. Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53(4):343–367

    Article  Google Scholar 

  61. Yuan Y (2013) Lagrangian multi-class traffic state estimation. Ph.D. thesis, Delft University of Technology

    Google Scholar 

  62. Djurić, PM, Kotecha JH, Zhang J, Huang Y, Ghirmai T, Bugallo MF, Miguez J (2003) Particle filtering. IEEE Signal Process Mag 20(5):19–38

    Google Scholar 

  63. Gu F, Hu X (2008) Towards applications of particle filters in wildfire spread simulation. In: Proceedings of the 2008 winter simulation conference, Miami, FL, USA, pp 2852–2860

    Google Scholar 

  64. Xue H, Gu F, Hu X (2012) Data assimilation using sequential Monte Carlo methods in wildfire spread simulation. ACM Trans Model Comput Simul 22(4): 1–23, 25

    Google Scholar 

  65. Hu X, Sun Y, Ntaimo L (2012) DEVS-FIRE: design and application of formal discrete event wildfire spread and suppression models. SIMULATION: Trans Soc Model Simul Int 88(3):259–279

    Google Scholar 

  66. Wang M, Hu X (2015) Data assimilation in agent based simulation of smart environments using particle filters. Simul Model Pract Theory 56:36–54

    Article  Google Scholar 

  67. Xie X, van Lint H, Verbraeck A (2018) A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters. Transp Res Part C: Emerg Technol 92:364–391

    Article  Google Scholar 

  68. Li BH, Zhang L, Chai XD (2010) Introduction to cloud manufacturing, no. 4. ZTE Communications

    Google Scholar 

  69. Zhang L, Luo YL, Tao F, Li BH, Ren L, Zhang XS, Guo H, Cheng Y, Hu AR (2014) Cloud manufacturing: a new manufacturing paradigm. Enterp Inf Syst 8(2):167–187

    Article  Google Scholar 

  70. Li F, Liao TW, Zhang L (2019) Two-level multi-task scheduling in a cloud manufacturing environment. Robot Comput Integr Manuf 56:127–139

    Article  Google Scholar 

  71. Li F, Zhang L, Liao TW, Liu YL (2019) Multi-objective optimisation of multi-task scheduling in cloud manufacturing. Int J Prod Res 57(12):3847–3863

    Article  Google Scholar 

  72. Zhou LF, Zhang L, Ren L, Wang J (2019) Real-time scheduling of cloud manufacturing services based on dynamic data-driven simulation. IEEE Trans Ind Inf 15(9)

    Google Scholar 

  73. Kück M, Ehm J, Hildebrandt T, Freitag M, Frazzon EM (2016) Potential of data-driven simulation-based optimization for adaptive scheduling and control of dynamic manufacturing systems. In: 2016 winter simulation conference, Washington, DC, pp 2820–2831

    Google Scholar 

  74. Keller N, Hu X (2016) Data driven simulation modeling for mobile agent-based systems. In: 2016 symposium on theory of modeling and simulation, Pasadena, CA, pp 1–8

    Google Scholar 

  75. Zeigler BP, Muzy A, Kofman E (2019) Introduction to systems modeling concepts. Theory of modeling and simulation. Academic Press, Orlando, pp 3–25

    Google Scholar 

  76. Chen S, Wang H (2014) SAR target recognition based on deep learning. In: Proceedings of the 2014 IEEE international conference on data science and advanced analytics, pp 541–547

    Google Scholar 

  77. Zeigler BP, Muzy A, Kofman E (2018) Theory of modeling and simulation: discrete event & iterative system computational foundations, 3rd edn. Elsevier

    Google Scholar 

  78. Seo C, Zeigler BP, Coop R. Kim D (2013) DEVS modeling and simulation methodology with MS4 Me software. In: Symposium on theory of modeling & simulation, Spring Sim San Diego

    Google Scholar 

  79. Diehl PU, Zarrella G, Cassidy A (2016) Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware. arXiv:1601.04187 [cs:NE]

  80. Rajendran B, Sebastian A (2019) Low-power neuromorphic hardware for signal processing applications: a review of architectural and system-level design approaches. IEEE Signal Process Mag 36(6):97–110. https://doi.org/10.1109/MSP.2019.2933719

    Article  Google Scholar 

  81. Opris I, Casanova MF (2017) Prefrontal cortical microcircuits support the emergence of mind. In: Springer series in cognitive and neural systems, vol 11

    Google Scholar 

  82. Grinblat GL, Herman A, Kofman E (2011) Quantized state simulation of spiking neural networks. Simulation: Trans Soc Model Simul Int 88(3):299–313

    Google Scholar 

  83. Muzy A, Zeigler BP (2020) Morphisms for lumping finite-size linear system realizations of componentized neural networks, https://hal.archives-ouvertes.fr/hal-02429240v4

  84. Jarvis D (2020) Machine learning of an approximate morphism of an electronic warfare simulation component by https://springsim.conferencespot.org/event-data. Accessed 20 Jan 2020

  85. Zeigler BP, Muzy A (2017) Temporal modeling of neural net input/output behaviors: the case of XOR. Systems 5(1):7

    Article  Google Scholar 

  86. Panda P, Srinivasa N (2018) Learning to recognize actions from limited training examples using a recurrent spiking neural model. Front Neurosci 12:126. https://doi.org/10.3389/fnins.2017.00126

  87. Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: International conference on machine learning. jmlr.org

    Google Scholar 

  88. Li M et al (2018) Spike-timing pattern operates as gamma-distribution across cell types. Accessed 20 Jan 2020

    Google Scholar 

  89. Xie K (2016) Brain computation is organized via power-of-two-based permutation logic frontiers. Neuroscience. https://doi.org/10.3389/fnsys.2016.00095

  90. Abbott TP, Vogels LF (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25:10786–10795

    Article  Google Scholar 

  91. Zeigler BP (2020) Hybrid iterative system specification of cyberphysical systems: neurocognitive behavior application. SpringSim

    Google Scholar 

  92. Petersen SE, Sporns O (2018) Brain networks and cognitive architectures, vol 88, Issue 1, pp 207–219

    Google Scholar 

  93. Sargent RG (2011) Verification and validation of simulation models. In: Proceedings of the 2011 winter simulation conference, Phoenix, AZ, USA, pp 183–198

    Google Scholar 

  94. Junghanns A, Blochwitz T (2017) FMI is great—But not magic. In: FMI user meeting, 12th international modelica conference, 15–17 May 2017, Prague, Czech Republic. https://fmi-standard.org/literature/. Accessed 20 Nov 2020

  95. Junglas P, Pawletta T (2019) Non-standard Queuing Policies: Definition of ARGESIM Benchmark C22. SNE—Simulation Notes Europe 29(3):111–115. https://doi.org/10.11128/sne.29.bn22.10481

  96. Folkerts H, Pawletta T, Deatcu C, Hartmann S (2019) Python-based eSES/MB framework: model specification and automatic model generation for multiple simulators. SNE Simul Notes Europe 29(4):207–215

    Article  Google Scholar 

  97. Blochwitz T, Otter M, Arnold M, Bausch C, Clauß C, Elmqvist H, Junghanns A, Mauss J, Monteiro M, Neidhold T, Neumerkel D, Olsson H, Peetz JV, Wolf S (2011) The functional mockup interface for tool independent exchange of simulation models. In: Proceedings of the 8th international modelica conference. Modelica conference, 2011, March, Dresden, Germany, pp 105–114. https://doi.org/10.3384/ecp11063105

  98. Blochwitz T, Otter M, Akesson J, Arnold M, Clauß C, Elmqvist H, Friedrich M Junghanns A, Mauss J, Neumerkel D, Olsson H, Viel A (2012) Functional mockup interface 2.0: the standard for tool independent exchange of simulation models. In: Proceedings of the 9th international modelica conference. Modelica conference, 2012, Sept, Munich, Germany, pp 173–184. https://doi.org/10.3384/ecp12076173

  99. Modelica Association (2020). Modelica Association project system structure and parametrization (SSP). Modelica Association c/o PELAB, IDA, Linköpings Universitet, Linköping, Sweden

    Google Scholar 

  100. Cremona F, Lohstroh M, Broman D, Lee EA, Masin M, Tripakis S (2019) Hybrid co-simulation: it’s about time. SoftwSyst Model 18:1655–1679. https://doi.org/10.1007/s10270-017-0633-6

    Article  Google Scholar 

  101. Folkerts H, Pawletta T, Deatcu C (2021) Model generation for multiple simulators using SES/MB and FMI. SNE - Simulation Notes Europe 31(1):25–32. https://doi.org/10.11128/sne.31.tn.10554

  102. Schmidt A (2018) Variantenmanagement in der Modellbildung und simulation unterVerwendung des SES/MB frameworks [Variant management in modeling and simulation using the SES/MB framework]. Ph.D. thesis, ASIM FBS—Advances in Simulation No. 30, ARGESIM Publisher Vienna, Austria, 10.111.28/fbs.30

    Google Scholar 

  103. Pawletta T, Schmidt A, Zeigler BP, Durak U (2016) Extended variability modeling using system entity structure ontology within MATLAB/simulink. In: Proceedings of SCS International SpringSim/ANSS 2016, Pasadena/CA, USA, SCS, pp 62–69

    Google Scholar 

  104. RG CEA (2020) Python-based SES/MB infrastructure. Research Group CEA, Wismar University of Applied Sciences. https://www.github.com/cea-wismar/SESMB_Inf_Python. Accessed 17 Sep 2022

  105. Mittal S, Durak U, Ören T (2017) Guide to simulation-based disciplines: advancing our computational future. Springer AG

    Google Scholar 

  106. Areekkuzhiyil S (2017) Emergence of new disciplines. Edutracks 17(4):20–22

    Google Scholar 

  107. Tolk A, Ören T (2017) The profession of modeling and simulation: discipline, ethics, education, vocation, societies, and economics. Wiley

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Texas at Dallas, Richardson, TX, USA

    Paul Fishwick

  2. Old Dominion University, Norfolk, VA, USA

    Saikou Y. Diallo

  3. German Aerospace Center, Cologne, Germany

    Umut Durak

  4. Midea Cloud Tech Co., Ltd, Beijing, China

    Baocun Hou

  5. Beihang University, Beijing, China

    Bo Hu Li & Lin Zhang

  6. CASICloud-Tech Co., Ltd., Beijing, China

    Chunhui Su & Yanguang Wang

  7. National University of Defense Technology, Changsha, China

    Xu Xie

  8. Duke University Medical Center, Durham, NC, USA

    Longfei Zhou

  9. University of Arizona, Tucson, AZ, USA

    Bernard P. Zeigler

  10. University of Applied Sciences in Wismar, Philipp-Müller-Str. 14, 23966, Wismar, Germany

    Thorsten Pawletta & Hendrik Folkerts

  11. The MITRE Corporation, Dayton, OH, USA

    Saurabh Mittal

Authors
  1. Paul Fishwick
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saikou Y. Diallo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Umut Durak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Baocun Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bo Hu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chunhui Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yanguang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xu Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Longfei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Bernard P. Zeigler
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Thorsten Pawletta
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Hendrik Folkerts
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Saurabh Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul Fishwick .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  2. Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA

    Bernard P. Zeigler

  3. The MITRE Corporation, Charlottesville, VA, USA

    Andreas Tolk

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Fishwick, P. et al. (2023). Core Research Areas. In: Ören, T., Zeigler, B.P., Tolk, A. (eds) Body of Knowledge for Modeling and Simulation. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-11085-6_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-11085-6_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11084-9

  • Online ISBN: 978-3-031-11085-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation