skip to main content
research-article

Context, Composition, Automation, and Communication: The C2AC Roadmap for Modeling and Simulation

Published: 13 August 2024 Publication History

Abstract

Simulation has become, in many application areas, a sine qua non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressing these limitations. The first is to provide better support for capturing, representing, and evaluating the context of simulation studies, including research questions, assumptions, requirements, and activities contributing to a simulation study. In addition, the composition of simulation models and other simulation studies’ products must be supported beyond syntactical coherence, including aspects of semantics and purpose, enabling their effective reuse. A higher degree of automating simulation studies will contribute to more systematic, standardized simulation studies and their efficiency. Finally, it is essential to invest increased effort into effectively communicating results and the processes involved in simulation studies to enable their use in research and decision making. These goals are not pursued independently of each other, but they will benefit from and sometimes even rely on advances in other sub-fields. In this article, we explore the basis and interdependencies evident in current research and practice and delineate future research directions based on these considerations.

References

[1]
Collaborative Research Center SFB-TRR 161. 2019. Quantitative Methods for Visual Computing. Retrieved January 27, 2023 from https://www.sfbtrr161.de/
[2]
Sameera Abar, Georgios K. Theodoropoulos, Pierre Lemarinier, and Gregory M. P. O’Hare. 2017. Agent based modelling and simulation tools: A review of the state-of-art software. Computer Science Review 24 (2017), 13–33.
[3]
ACM. 2020. Artifact Reviewing and Badging—Current. Retrieved June 21, 2024 from https://www.acm.org/publications/policies/artifact-review-and-badging-current
[4]
Shehzad Afzal, Sohaib Ghani, Hank C. Jenkins-Smith, David S. Ebert, Markus Hadwiger, and Ibrahim Hoteit. 2020. A visual analytics based decision making environment for COVID-19 modeling and visualization. In Proceedings of the 2020 IEEE Visualization Conference (VIS’20). IEEE, 86–90.
[5]
Gul Agha and Karl Palmskog. 2018. A survey of statistical model checking. ACM Transactions on Modeling and Computer Simulation 28, 1 (2018), 1–39.
[6]
Yasmine Ahmed, Cheryl A. Telmer, Gaoxiang Zhou, and Natasa Miskov-Zivanov. 2023. Context-aware knowledge selection and reliable model recommendation with ACCORDION. bioRxiv (2023).
[7]
Fatima N. Al-Aswadi, Huah Yong Chan, and Keng Hoon Gan. 2020. Automatic ontology construction from text: A review from shallow to deep learning trend. Artificial Intelligence Review 53, 6 (2020), 3901–3928.
[8]
Elvira Albert, Jesús Correas Fernández, Germán Puebla, and Guillermo Román-Díez. 2015. Quantified abstract configurations of distributed systems. Formal Aspects of Computing 27, 4 (2015), 665–699.
[9]
Alexander A. Alemi, François Chollet, Niklas Een, Geoffrey Irving, Christian Szegedy, and Josef Urban. 2016. DeepMath—Deep sequence models for premise selection. In Proceedings of the 30th International Conference on Neural Information Processing Systems(NIPS’16). 2243–2251.
[10]
Mohammed Ali, Ali Alqahtani, Mark W. Jones, and Xianghua Xie. 2019. Clustering and classification for time series data in visual analytics: A survey. IEEE Access 7 (2019), 181314–181338.
[11]
M. David Allen, Len Seligman, Barbara Blaustein, and Adriane Chapman. 2010. Provenance Capture and Use: A Practical Guide. Technical Report. Mitre Corporation, McLean VA.
[12]
Natalia Andrienko, Tim Lammarsch, Gennady Andrienko, Georg Fuchs, Daniel Keim, Silvia Miksch, and Andrea Rind. 2018. Viewing visual analytics as model building. In Computer Graphics Forum 37, 6 (2018), 275–299.
[13]
Sven Apel, Don S. Batory, Christian Kästner, and Gunter Saake. 2013. Feature-Oriented Software Product Lines: Concepts and Implementation. Springer. I–XVI.
[14]
Raul Astudillo and Peter Frazier. 2020. Multi-attribute Bayesian optimization with interactive preference learning. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 4496–4507.
[15]
Colin Atkinson and Thomas Kuhne. 2003. Model-driven development: A metamodeling foundation. IEEE Software 20, 5 (2003), 36–41.
[16]
Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. DBpedia: A nucleus for a web of open data. In The Semantic Web, Karl Aberer, Key-Sun Choi, Natasha Noy, Dean Allemang, Kyung-Il Lee, Lyndon Nixon, Jennifer Golbeck, Peter Mika, Diana Maynard, Riichiro Mizoguchi, Guus Schreiber, and Philippe Cudré-Mauroux (Eds.). Springer, Berlin, Germany, 722–735.
[17]
Daniel Ayllón, Steven F. Railsback, Cara Gallagher, Jacqueline Augusiak, Hans Baveco, Uta Berger, Sandrine Charles, Romina Martin, Andreas Focks, Nika Galic, Chun Liu, Emiel van Loon, Jacob Nabe-Nielsen, Cyril Piou, J. Gareth Polhill, Thomas G. Preuss, Viktoria Radchuk, Amelie Schmolke, Julita Stanicka-Michalak, Pernille Thorbeck, and Volker Grimm. 2021. Keeping modelling notebooks with TRACE: Good for you and good for environmental research and management support. Environmental Modelling & Software 136 (2021), 104932.
[18]
Gianfranco Balbo. 2001. Introduction to stochastic Petri nets. In Lectures on Formal Methods and Performance Analysis. Springer, 84–155.
[19]
Osman Balci. 2012. A life cycle for modeling and simulation. Simulation 88, 7 (2012), 870–883.
[20]
Lynne P. Baldwin, Tillal Eldabi, and Ray J. Paul. 2004. Simulation in healthcare management: A soft approach (MAPIU). Simulation, Modelling, Practice and Theory 12, 7-8 (2004), 541–557.
[21]
Cécile Barnaud, François Bousquet, and Guy Trebuil. 2008. Multi-agent simulations to explore rules for rural credit in a highland farming community of northern Thailand. Ecological Economics 66, 4 (2008), 615–627.
[22]
Olivier Barreteau, Pieter Bots, Katherine Daniell, Michel Etienne, Pascal Perez, Cécile Barnaud, Didier Bazile, Nicolas Becu, Jean-Christophe Castella, William’s Daré, and Guy Trebuil. 2017. Participatory approaches. In Simulating Social Complexity: A Handbook. Springer International Publishing, 253–292.
[23]
Oliver Barreteau. 2003. Our companion modelling approach. Journal of Artificial Societies and Social Simulation 6, 2 (2003), 1. https://jasss.soc.surrey.ac.uk/6/2/1.html
[24]
Fernando J. Barros. 1997. Modeling formalisms for dynamic structure systems. ACM Transactions on Modeling and Computer Simulation 7, 4 (Oct. 1997), 501–515.
[25]
Robert G. Bartholet, David C. Brogan, Paul F. Reynolds, and Joseph C. Carnahan. 2004. In search of the philosopher’s stone: Simulation composability versus component-based software design. In Proceedings of the Fall Simulation Interoperability Workshop.
[26]
Ezio Bartocci, Jyotirmoy Deshmukh, Alexandre Donzé, Georgios Fainekos, Oded Maler, Dejan Ničković, and Sriram Sankaranarayanan. 2018. Specification-based monitoring of cyber-physical systems: A survey on theory, tools and applications. In Lectures on Runtime Verification. Springer, Cham, 135–175.
[27]
Ezio Bartocci, Cristinel Mateis, Eleonora Nesterini, and Dejan Nickovic. 2022. Survey on mining signal temporal logic specifications. Information and Computation 289, Part A (2022), 104957.
[28]
Leonardo J. Basso, Marcel Goic, Marcelo Olivares, Denis Sauré, Charles Thraves, Aldo Carranza, Gabriel Y. Weintraub, Julio Covarrubias, Cristian Escobedo, Natalia Jara, Antonio Morena, Demian Arancibia, Manuel Fuenzalida, Juan Pablo Uribe, Felipe Zuniga, Marcela Zuniga, Miguel O’Ryan, Emilio Santelices, Juan Pablo Torres, Magdalena Badal, Mirko Bozanic, Sebastian Cancino-Espinoza, Eduardo Lara, and Ignasi Neira. 2023. Analytics saves lives during the covid crisis in chile. INFORMS Journal on Applied Analytics 53, 1 (2023), 9–31.
[29]
Gregory Batt, Jeremy T. Bradley, Roland Ewald, François Fages, Holger Hermans, Jane Hillston, Peter Kemper, Alke Martens, Pieter Mosterman, Flemming Nielson, Oleg Sokolsky, and Adelinde M. Uhrmacher. 2006. Working groups’ report: The challenge of combining simulation and verification. In Dagstuhl Seminar Proc. 06161: Simulation and Verification of Dynamic Systems.
[30]
Maximilian Beikirch, Simon Cramer, Martin Frank, Philipp Otte, Emma Pabich, and Torsten Trimborn. 2018. Simulation of stylized facts in agent-based computational economic market models. arXiv:1812.02726 (2018). https://ideas.repec.org/p/arx/papers/1812.02726.html
[31]
Frank T. Bergmann, Richard Adams, Stuart Moodie, Jonathan Cooper, Mihai Glont, Martin Golebiewski, Michael Hucka, Camille Laibe, Andrew K. Miller, David P. Nickerson, Brett G. Olivier, Nicolas Rodriguez, Herbert M. Sauro, Martin Scharm, Stian Soiland-Reyes, Dagmar Waltemath, Florent Yvon, and Nicolas Le Novère. 2014. COMBINE archive and OMEX format: One file to share all information to reproduce a modeling project. BMC Bioinformatics 15, 1 (2014), 1–9.
[32]
Hugues Bersini. 2012. UML for ABM. Journal of Artificial Societies and Social Simulation 15, 1 (2012), 9.
[33]
Ludwig von Bertalanffy. 1968. General System Theory: Foundations, Development, Applications. G. Braziller.
[34]
Alessandro Berti, Sebastiaan J Van Zelst, and Wil van der Aalst. 2019. Process mining for Python (PM4Py): Bridging the gap between process and data science. arXiv preprint arXiv:1905.06169 (2019).
[35]
Nicky Best, Nigel Dallow, and Timothy Montague. 2020. Prior elicitation. In Bayesian Methods in Pharmaceutical Research. Chapman & Hall/CRC, 87–109.
[36]
Martin Bicher, Christoph Urach, and Niki Popper. 2018. GEPOC ABM: A generic agent-based population model for Austria. In Proceedings of the 2018 Winter Simulation Conference (WSC’18). IEEE, 2656–2667.
[37]
Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A. Lozano. 2021. A review on outlier/anomaly detection in time series data. ACM Computing Surveys 54, 3 (April 2021), Article 56, 33 pages.
[38]
Michael L. Blinov, James R. Faeder, Byron Goldstein, and William S. Hlavacek. 2004. BioNetGen: Software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20, 17 (2004), 3289–3291.
[39]
Michael L. Blinov, James C. Schaff, Dan Vasilescu, Ion I. Moraru, Judy E. Bloom, and Leslie M. Loew. 2017. Compartmental and spatial rule-based modeling with virtual cell. Biophysical Journal 113, 7 (2017), 1365–1372.
[40]
Emma Blomkamp. 2018. The promise of co-design for public policy. Australian Journal of Public Administration 77, 4 (2018), 729–743.
[41]
Tom Blount, Adriane Chapman, Michael Johnson, and Bertram Ludascher. 2021. Observed vs. possible provenance (research track). In Proceedings of the 13th International Workshop on Theory and Practice of Provenance (TaPP’21). https://www.usenix.org/conference/tapp2021/presentation/blount
[42]
Defense Science Board. 2020. AD1155605—Task Force on Gaming, Exercising, Modeling, and Simulation (GEMS). U.S. Department of Defense, Washington, DC.
[43]
Paolo Bocciarelli, Andrea D’Ambrogio, Andrea Giglio, and Daniele Gianni. 2013. A SaaS-based automated framework to build and execute distributed simulations from SysML models. In Proceedings of the 2013 Winter Simulations Conference (WSC’13). 1371–1382.
[44]
Paolo Bocciarelli, Andrea D’Ambrogio, Alberto Falcone, Alfredo Garro, and Andrea Giglio. 2019. A model-driven approach to enable the simulation of complex systems on distributed architectures. SIMULATION 95, 12 (2019), 1185–1211.
[45]
Grady Booch, James Rumbaugh, and Ivar Jacobson. 1998. The Unified Modeling Language User Guide. Addison-Wesley.
[46]
Luca Bortolussi, Dimitrios Milios, and Guido Sanguinetti. 2016. Smoothed model checking for uncertain continuous-time Markov chains. Information and Computation 247 (2016), 235–253.
[47]
Louis Bouchet, Martin C. Thoms, and Melissa Parsons. 2022. Using causal loop diagrams to conceptualize groundwater as a social-ecological system. Frontiers in Environmental Science 10 (2022), 836206.
[48]
Pierre Boutillier, Mutaamba Maasha, Xing Li, Héctor F. Medina-Abarca, Jean Krivine, Jérôme Feret, Ioana Cristescu, Angus G. Forbes, and Walter Fontana. 2018. The Kappa platform for rule-based modeling. Bioinformatics 34, 13 (2018), i583–i592.
[49]
Lubos Brim, Petr Dluhos, David Safránek, and Tomas Vejpustek. 2014. STL\(^\ast\): Extending signal temporal logic with signal-value freezing operator. Information and Computation 236 (2014), 52–67.
[50]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.). Vol. 33. Curran Associates, 1877–1901. https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
[51]
Steven L. Brunton, Joshua L. Proctor, and J. Nathan Kutz. 2016. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences 113, 15 (2016), 3932–3937.
[52]
Richard Bubel, Antonio Flores-Montoya, and Reiner Hähnle. 2014. Analysis of executable software models. In Formal Methods for Executable Software Models. Lecture Notes in Computer Science, Vol. 8483. Springer, 1–25.
[53]
Kai Budde, Jacob Smith, Pia Wilsdorf, Fiete Haack, and Adelinde M. Uhrmacher. 2021. Relating simulation studies by provenance—developing a family of WNT signaling models. PLoS Computational Biology 17, 8 (2021), e1009227.
[54]
Terry W. Burns, D. John O’Connor, and Susan M. Stocklmayer. 2003. Science communication: A contemporary definition. Public Understanding of Science 12, 2 (2003), 183–202.
[55]
Pamela M. Burrage, Hasitha N. Weerasinghe, and Kevin Burrage. 2024. Using a library of chemical reactions to fit systems of ordinary differential equations to agent-based models: A machine learning approach. Numerical Algorithms 96 (2024), 1063–1077.
[56]
Zoya Bylinskii, Laura Herman, Aaron Hertzmann, Stefanie Hutka, and Yile Zhang. 2022. Towards better user studies in computer graphics and vision. arXiv:2206.11461 (2022).
[57]
Wentong Cai, Philipp Andelfinger, Luca Bortolussi, Christopher Carothers, Dong (Kevin) Jin, Till Köster, Michael Lees, Jason Liu, Margaret Loper, Alessandro Pellegrini, Wen Jun Tan, and Verena Wolf. 2023. Intelligent modeling and simulation lifecycle. In Computer Science Methods for Effective and Sustainable Simulation Studies (Dagstuhl Seminar 22401). DOI:
[58]
Wentong Cai, Christopher Carothers, David M. Nicol, and Adelinde M. Uhrmacher. 2023. Computer science methods for effective and sustainable simulation studies (Dagstuhl Seminar 22401). Dagstuhl Reports 12, 10 (2023), 1–60. DOI:
[59]
Francesca Cairoli, Fabio Anselmi, Alberto d’Onofrio, and Luca Bortolussi. 2023. Generative abstraction of Markov population processes. Theoretical Computer Science 977 (2023), 114169.
[60]
Olivier Casse. 2017. SysML in Action with Cameo Systems Modeler. Elsevier.
[61]
Rodrigo Castro, Joachim Denil, Jérôme Feret, Kresimir Matkovic, Niki Popper, Susan Sanchez, and Peter Sloot. 2023. Policy by simulation: Seeing is believing for interactive model co-creation and effective intervention. In Computer Science Methods for Effective and Sustainable Simulation Studies (Dagstuhl Seminar 22401). DOI:
[62]
François E. Cellier and Jurgen Greifeneder. 2013. Continuous System Modeling. Springer Science & Business Media.
[63]
María Victoria Cengarle, Hans Grönniger, and Bernhard Rumpe. 2009. Variability within modeling language definitions. In Model Driven Engineering Languages and Systems. Lecture Notes in Computer Science, Vol. 5795. Springer, 670–684. http://www.se-rwth.de/publications/Variability-within-Modeling-Language-Definitions.pdf
[64]
Deniz Cetinkaya, Alexander Verbraeck, and Mamadou D. Seck. 2012. Model transformation from BPMN to DEVS in the MDD4MS framework. In Proceedings of the 2012 Symposium on Theory of Modeling and Simulation—DEVS Integrative M&S Symposium (TMS/DEVS’12). Article 28, 6 pages.
[65]
Li Chen and Pearl Pu. 2004. Survey of Preference Elicitation Methods. Technical Report IC/2004/67. Ecole Politechnique Federale de Lausanne (EPFL).
[66]
Michele Chinosi and Alberto Trombetta. 2012. BPMN: An introduction to the standard. Computer Standards & Interfaces 34, 1 (2012), 124–134.
[67]
Federica Ciocchetta and Jane Hillston. 2009. Bio-PEPA: A framework for the modelling and analysis of biological systems. Theoretical Computer Science 410, 33-34 (2009), 3065–3084.
[68]
Tony Clark, Mark van den Brand, Benoit Combemale, and Bernhard Rumpe. 2015. Conceptual model of the globalization for domain-specific languages. In Globalizing Domain-Specific Languages. Lecture Notes in Computer Science, Vol. 9400. Springer, 7–20. http://www.se-rwth.de/publications/Conceptual-Model-of-the-Globalization-for-Domain-Specific-Languages.pdf
[69]
Tony Clark, Andy Evans, Paul Sammut, and James Willans. 2004. An executable metamodelling facility for domain specific language design. In Proceedings of the 4th OOPSLA Workshop on Domain-Specific Modeling.
[70]
Edmund M. Clarke, Jr., Orna Grumberg, and Doron A. Peled. 1999. Model Checking. MIT Press, Cambridge, MA.
[71]
Paul Clements and Linda Northrop. 2001. Software Product Lines: Practices & Patterns. Addison Wesley Longman.
[72]
Benoit Combemale, Robert France, Jean-Marc Jézéquel, Bernhard Rumpe, James Steel, and Didier Vojtisek. 2016. Engineering Modeling Languages: Turning Domain Knowledge into Tools. Chapman & Hall/CRC Innovations in Software Engineering and Software Development Series. Chapman & Hall/CRC.
[73]
Michael R. Crusoe, Sanne Abeln, Alexandru Iosup, Peter Amstutz, John Chilton, Nebojša Tijanić, Hervé Ménager, Stian Soiland-Reyes, Bogdan Gavrilović, Carole Goble, and the CWL Community. 2022. Methods included: Standardizing computational reuse and portability with the common workflow language. Communications of the ACM 65, 6 (May 2022), 54–63.
[74]
Víctor Cuevas-Vicenttín, Saumen C. Dey, Sven Köhler, Sean Riddle, and Bertram Ludäscher. 2012. Scientific workflows and provenance: Introduction and research opportunities. Datenbank-Spektrum 12, 3 (2012), 193–203.
[75]
Olivier Dalle. 2006. OSA: An open component-based architecture for discrete-event simulation. In Proceedings of the 20th European Conference on Modeling and Simulation.
[76]
Jônathan W. V. Dambros, Jorge O. Trierweiler, and Marcelo Farenzena. 2019. Oscillation detection in process industries—Part I: Review of the detection methods. Journal of Process Control 78 (2019), 108–123.
[77]
Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. 2021. Parallel Bayesian optimization of multiple noisy objectives with expected hypervolume improvement. Advances in Neural Information Processing Systems 34 (2021), 2187–2200.
[78]
Paul K. Davis. 2016. Capabilities for Joint Analysis in the Department of Defense: Rethinking Support for Strategic Analysis. RAND Corporation, Santa Monica, CA.
[79]
Paul K. Davis, James Bigelow, and Jimmie McEver. 2000. Exploratory Analysis and a Case History of Multiresolution, Multiperspective Modeling. Report No. RP-925. Rand Corporation.
[80]
Frank De Boer, Ferruccio Damiani, Reiner Hähnle, Einar Broch Johnsen, and Eduard Kamburjan (Eds.). 2024. Active Object Languages: Current Research Trends. Lecture Notes in Computer Science, Vol. 14360. Springer, Cham.
[81]
Frank de Boer, Crystal Chang Din, Kiko Fernandez-Reyes, Reiner Hähnle, Ludovic Henrio, Einar Broch Johnsen, Ehsan Khamespanah, Justine Rochas, Vlad Serbanescu, Marjan Sirjani, and Albert Mingkun Yang. 2017. A survey of active object languages. ACM Computing Surveys 50, 5 (Oct. 2017), Article 76, 39 pages.
[82]
Juan de Lara, Hans Vangheluwe, and Manuel Alfonseca. 2004. Meta-modelling and graph grammars for multi-paradigm modelling in AToM3. Software & Systems Modeling 3, 3 (Aug. 2004), 194–209.
[83]
Ewa Deelman, Karan Vahi, Gideon Juve, Mats Rynge, Scott Callaghan, Philip J. Maechling, Rajiv Mayani, Weiwei Chen, Rafael Ferreira da Silva, Miron Livny, and Kent Wenger. 2015. Pegasus, a workflow management system for science automation. Future Generation Computer Systems 46 (2015), 17–35.
[84]
Joachim Denil, Stefan Klikovits, Pieter J. Mosterman, Antonio Vallecillo, and Hans Vangheluwe. 2017. The experiment model and validity frame in M&S. In Proceedings of the Symposium on Theory of Modeling and Simulation. 1–12.
[85]
Crystal Chang Din, Richard Bubel, and Reiner Hähnle. 2015. KeY-ABS: A deductive verification tool for the concurrent modelling language ABS. In Automated Deduction. Lecture Notes in Computer Science, Vol. 9195. Springer, 517–526.
[86]
Alexandre Donzé, Thomas Ferrere, and Oded Maler. 2013. Efficient robust monitoring for STL. In Proceedings of the International Conference on Computer Aided Verification. 264–279.
[87]
Alexandre Donzé, Oded Maler, Ezio Bartocci, Dejan Nickovic, Radu Grosu, and Scott A. Smolka. 2012. On temporal logic and signal processing. In Automated Technology for Verification and Analysis. Lecture Notes in Computer Science, Vol. 7561. Springer, 92–106.
[88]
Dominique Douglas-Smith, Takuya Iwanaga, Barry F. W. Croke, and Anthony J. Jakeman. 2020. Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques. Environmental Modelling & Software 124 (2020), 104588.
[89]
Sašo Džeroski and Ljupčo Todorovski. 2008. Equation discovery for systems biology: Finding the structure and dynamics of biological networks from time course data. Current Opinion in Biotechnology 19, 4 (2008), 360–368.
[90]
Wouter Edeling, Hamid Arabnejad, Robbie Sinclair, Diana Suleimenova, Krishnakumar Gopalakrishnan, Bartosz Bosak, Derek Groen, Imran Mahmood, Daan Crommelin, and Peter V. Coveney. 2021. The impact of uncertainty on predictions of the CovidSim epidemiological code. Nature Computational Science 1 (Feb. 2021), 128–135.
[91]
Christian Eichner, Arne Bittig, Heidrun Schumann, and Christian Tominski. 2014. Analyzing simulations of biochemical systems with feature-based visual analytics. Computers & Graphics 38 (2014), 18–26.
[92]
Hilding Elmqvist and Sven-Erik Mattsson. 1997. MODELICA—The next generation modeling language: An international design effort. In Proceedings of 1st World Congress of System Simulation. 1–3.
[93]
Julien Emmanuel, Matthieu Moy, Ludovic Henrio, and Grégoire Pichon. 2021. S4BXI: The MPI-ready portals 4 simulator. In Proceedings of the 29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS’21). IEEE, 1–8.
[94]
Ahmet Erdemir, Trent M. Guess, Jason Halloran, Srinivas C. Tadepalli, and Tina M. Morrison. 2012. Considerations for reporting finite element analysis studies in biomechanics. Journal of Biomechanics 45, 4 (2012), 625–633.
[95]
Andy Evans, Jean-Michel Bruel, Robert France, Kevin Lano, and Bernhard Rumpe. 1998. Making UML precise. In Proceedings of the OOPSLA’98 Workshop on “Formalizing UML: Why and How?”
[96]
Andy Evans, Robert France, Kevin Lano, and Bernhard Rumpe. 1999. Meta-modelling semantics of UML. In Behavioral Specifications of Businesses and Systems, H. Kilov, B. Rumpe, and I. Simmonds (Eds.). Kluver Academic Publishers, 45–60.
[97]
Roland Ewald and Adelinde M. Uhrmacher. 2014. SESSL: A domain-specific language for simulation experiments. ACM Transactions on Modeling and Computer Simulation 24, 2 (2014), 1–25.
[98]
James R. Faeder, Michael L. Blinov, and William S. Hlavacek. 2009. Rule-based modeling of biochemical systems with BioNetGen. In Systems Biology. Springer, 113–167.
[99]
Niclas Feldkamp, Soeren Bergmann, and Steffen Strassburger. 2020. Knowledge discovery in simulation data. ACM Transactions on Modeling and Computer Simulation 30, 4 (Nov. 2020), Article 24, 25 pages.
[100]
Niclas Feldkamp, Soeren Bergmann, and Steffen Strassburger. 2020. Knowledge discovery in simulation data. ACM Transactions on Modeling and Computer Simulation 30, 4 (Nov. 2020), Article 24, 25 pages.
[101]
Mingbin Feng and Jeremy Staum. 2017. Green simulation: Reusing the output of repeated experiments. ACM Transactions on Modeling and Computer Simulation 27, 4 (Oct. 2017), Article 23, 28 pages.
[102]
Neil M. Ferguson, Daniel Laydon, Gemma Nedjati-Gilani, Natsuko Imai, Kylie Ainslie, Marc Baguelin, Sangeeta Bhatia, Adhiratha Boonyasiri, Zulma Cucunubá, Gina Cuomo-Dannenburg, Amy Dighe, Ilaria Dorigatti, Han Fu, Katy Gaythorpe, Will Green, Arran Hamlet, Wes Hinsley, Lucy C. Okell, Sabine van Elsland, Hayley Thompson, Robert Verity, Erik Volz, Haowei Wang, Yuanrong Wang, Patrick G. T. Walker, Caroline Walters, Peter Winskill, Charles Whittaker, Christl A. Donnelly, Steven Riley, and Azra C. Ghani. 2020. Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand. Imperial College COVID-19 Response Team London.
[103]
Martin Fowler. 2010. Domain-Specific Languages. Pearson Education.
[104]
Martin Fowler and Kendall Scott. 1997. UML Distilled: Applying the Standard Object Modeling Language. Addison-Wesley Longman Ltd., Essex, UK.
[105]
Peter I. Frazier, J. Massey Cashore, Ning Duan, Shane G. Henderson, Alyf Janmohamed, Brian Liu, David B. Shmoys, Jiayue Wan, and Yujia Zhang. 2022. Modeling for COVID-19 college reopening decisions: Cornell, a case study. Proceedings of the National Academy of Sciences 119, 2 (2022), e2112532119.
[106]
Sanford. Friedenthal, Alan Moore, and Rick Steiner. 2011. A Practical Guide to SysML: The Systems Modeling Language. Elsevier Science. http://books.google.de/books?id=4xz6Fx50zwcC
[107]
Richard Fujimoto, Conrad Bock, Wei Chen, Ernest Page, and Jitesh H. Panchal. 2017. Research Challenges in Modeling and Simulation for Engineering Complex Systems. Springer.
[108]
Daniel Garcia-Vicuña, Laida Esparza, and Fermin Mallor. 2022. Hospital preparedness during epidemics using simulation: The case of COVID-19. Central European Journal of Operations Research 30, 1 (2022), 213–249.
[109]
Tom Gebhardt, Vasundra Touré, Dagmar Waltemath, Olaf Wolkenhauer, and Martin Scharm. 2022. Exploring the evolution of biochemical models at the network level. PLoS One 17, 3 (March 2022), e0265735.
[110]
Philippe J. Giabbanelli. 2023. GPT-based models meet simulation: How to efficiently use large-scale pre-trained language models across simulation tasks. arXiv:2306.13679 [cs.HC] (2023).
[111]
Elena Giachino, Cosimo Laneve, and Michael Lienhardt. 2016. A framework for deadlock detection in core ABS. Software and Systems Modeling 15, 4 (2016), 1013–1048.
[112]
Stephen Gilmore and Jane Hillston. 1994. The PEPA workbench: A tool to support a process algebra-based approach to performance modelling. Computer Performance Evaluation 794 (1994), 353–368.
[113]
Javier González, Zhenwen Dai, Andreas Damianou, and Neil D. Lawrence. 2017. Preferential Bayesian optimization. In Proceedings of the International Conference on Machine Learning. 1282–1291.
[114]
Katharina Görlach, Mirko Sonntag, Dimka Karastoyanova, Frank Leymann, and Michael Reiter. 2011. Conventional workflow technology for scientific simulation. Guide to e-Science: Next Generation Scientific Research and Discovery. Computer Communications and Networks. Springer, 323–352.
[115]
Volker Grimm, Jacqueline Augusiak, Andreas Focks, Béatrice M. Frank, Faten Gabsi, Alice S. A. Johnston, Chun Liu, Benjamin T. Martin, Mattia Meli, Viktoriia Radchuk, Pernille Thorbek, and Steven F. Railsback. 2014. Towards better modelling and decision support: documenting model development, testing, and analysis using TRACE. Ecological Modelling 280 (2014), 129–139.
[116]
Volker Grimm, Gary Polhill, and Julia Touza. 2017. Documenting social simulation models: The ODD protocol as a standard. In Simulating Social Complexity. Springer, 349–365.
[117]
Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Juergen Groeneveld, Alice S. A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, J. Gareth Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele, and Daniel Ayllón. 2020. The ODD protocol for describing Agent-Based and other simulation Models: A second update to improve clarity, Replication, and Structural Realism. Journal of Artificial Societies and Social Simulation 23, 2 (2020).
[118]
Gerrit Großmann, Michael Backenköhler, and Verena Wolf. 2020. Importance of interaction structure and stochasticity for epidemic spreading: A COVID-19 case study. In Quantitative Evaluation of Systems. Lecture Notes in Computer Science, Vol. 12289. Springer, 211–229.
[119]
Lars Guenther and Marina Joubert. 2017. Science communication as a field of research: Identifying trends, challenges and gaps by analysing research papers. Journal of Science Communication 16, 2 (2017), A02.
[120]
Yue Guo, Wei Qiu, Yizhong Wang, and Trevor Cohen. 2021. Automated lay language summarization of biomedical scientific reviews. In Proceedings of the AAAI Conference on Artificial Intelligence. 160–168.
[121]
Lea Gütebier, Tim Bleimehl, Ron Henkel, Jamie Munro, Sebastian Müller, Axel Morgner, Jakob Laenge, Anke Pachauer, Alexander Erdl, Jens Weimar, Kirsten Walther Langendorf, Vincent Vialard, Thorsten Liebig, Martin Preusse, Dagmar Waltemath, and Alexander Jarasch. 2022. CovidGraph: A graph to fight COVID-19. Bioinformatics 38, 20 (2022), 4843–4845.
[122]
Fiete Haack, Kai Budde, and Adelinde M. Uhrmacher. 2020. Exploring mechanistic and temporal regulation of LRP6 endocytosis in canonical WNT signaling. Journal of Cell Science 133, 15 (Aug. 2020), jcs243675.
[123]
Jussi Hakanen, Sanjin Radoš, Giovanni Misitano, Bhupinder S. Saini, Kaisa Miettinen, and Krešimir Matković. 2022. Interactivized: Visual interaction for better decisions with interactive multiobjective optimization. IEEE Access 10 (2022), 33661–33678.
[124]
Hannes Hansen and Martin N. Hebart. 2022. Semantic features of object concepts generated with GPT-3. arXiv:2202.03753 (2022).
[125]
Alison Harper, Navonil Mustafee, and Mike Yearworth. 2021. Facets of trust in simulation studies. European Journal of Operational Research 289, 1 (2021), 197–213.
[126]
Tobias Helms, Roland Ewald, Stefan Rybacki, and Adelinde M. Uhrmacher. 2015. Automatic runtime adaptation for component-based simulation algorithms. ACM Transactions on Modeling and Computer Simulation 26, 1 (2015), 1–24.
[127]
Tobias Helms, Tom Warnke, Carsten Maus, and Adelinde M. Uhrmacher. 2017. Semantics and efficient simulation algorithms of an expressive multilevel modeling language. ACM Transactions on Modeling and Computer Simulation 27, 2 (2017), 1–25.
[128]
Ron Henkel, Robert Hoehndorf, Tim Kacprowski, Christian Knüpfer, Wolfram Liebermeister, and Dagmar Waltemath. 2018. Notions of similarity for systems biology models. Briefings in Bioinformatics 19, 1 (2018), 77–88.
[129]
Ludovic Henrio and Justine Rochas. 2017. Multiactive objects and their applications. Logical Methods in Computer Science 13, 4 (2017), 12.
[130]
Melanie Herschel, Ralf Diestelkämper, and Houssem Ben Lahmar. 2017. A survey on provenance: What for? What form? What from? VLDB Journal 26, 6 (Dec. 2017), 881–906.
[131]
Jane Hillston. 2005. Process algebras for quantitative analysis. In Proceedings of the 20th Annual IEEE Symposium on Logic in Computer Science (LICS’05). IEEE, 239–248.
[132]
Jane Hillston, Andreas L. Opdahl, and Rob Pooley. 1991. A case study using the IMSE experimentation tool. In Advanced Information Systems Engineering. Lecture Notes in Computer Science, Vol. 498. Springer, 284–306.
[133]
Jan Himmelspach and Adelinde M. Uhrmacher. 2007. Plug’n simulate. In Proceedings of the 40th Annual Simulation Symposium (ANSS’07). IEEE, 137–143.
[134]
Katrin Hölldobler, Oliver Kautz, and Bernhard Rumpe. 2021. MontiCore Language Workbench and Library Handbook: Edition 2021. Shaker Verlag. http://www.monticore.de/handbook.pdf
[135]
Stefan Hoops, Sven Sahle, Ralph Gauges, Christine Lee, Jürgen Pahle, Natalia Simus, Mudita Singhal, Liang Xu, Pedro Mendes, and Ursula Kummer. 2006. COPASI—A complex pathway simulator. Bioinformatics 22, 24 (2006), 3067–3074.
[136]
Sebastian Höpfl, Jürgen Pleiss, and Nicole E. Radde. 2023. Bayesian estimation reveals that reproducible models in systems biology get more citations. Scientific Reports 13, 1 (2023), 2695.
[137]
Fred Howell and Ross McNab. 1998. SimJava: A discrete event simulation library for Java. Simulation Series 30 (1998), 51–56.
[138]
Michael Hucka, Andrew Finney, Herbert M. Sauro, Hamid Bolouri, John C. Doyle, Hiroaki Kitano, Adam P. Arkin, Benjamin J. Bornstein, Dennis Bray, Athel Cornish-Bowden, Andrés A. Cuellar, S. Dronov, Ernst D. Gilles, Martin Ginkel, Vishal Gor, I. Goryanin, Warren J. Hedley, Charlie Hodgman, Jan H. Hofmeyr, Peter J. Hunter, Navtej S. Juty, Jay L. Kasberger, Andreas Kremling, Ursula Kummer, Nicolas Le Novère, Leslie M. Loew, Daniel Lucio, Pedro Mendes, Eric Minch, Eric D. Mjolsness, Yuko Nakayama, Melanie R. Nelson, Poul F. Nielsen, Sakurada Tsukasa, James C. Schaff, Bruce E. Shapiro, Thomas S. Shimizu, Hugh D. Spence, Joerg Stelling, Kouichi Takahashi, Masaru Tomita, John Wagner, James Wang, and the rest of the SBML Forum. 2003. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 19, 4 (2003), 524–531.
[139]
Susan R. Hunter, Eric A. Applegate, Viplove Arora, Bryan Chong, Kyle Cooper, Oscar Rincón-Guevara, and Carolina Vivas-Valencia. 2019. An introduction to multiobjective simulation optimization. ACM Transactions on Modeling and Computer Simulation 29, 1 (2019), Article 7, 36 pages.
[140]
Mohammad Hussain, Nafiseh Masoudi, Gregory Mocko, and Chris Paredis. 2022. Approaches for Simulation Model Reuse in Systems Design—A Review. SAE Technical Paper2022-01-0355. SAE International.
[141]
Dmitry Ivanov. 2020. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review 136 (2020), 101922.
[142]
Ilya Jackson and Maria Jesus Saenz. 2023. From natural language to simulations: Applying GPT-3 codex to automate simulation modeling of logistics systems. arXiv:2202.12107 [cs.AI] (2023).
[143]
Nico Jansen, Jerome Pfeiffer, Bernhard Rumpe, David Schmalzing, and Andreas Wortmann. 2022. The language of SysML v2 under the magnifying glass. Journal of Object Technology 21, 3 (July 2022), 1–15.
[144]
Marco A. Janssen, Calvin Pritchard, and Allen Lee. 2020. On code sharing and model documentation of published individual and agent-based models. Environmental Modelling & Software 134 (2020), 104873.
[145]
Kurt Jensen. 1996. Coloured Petri Nets: Basic Concepts, Analysis Methods and Practical Use. Vol. 1. Springer Science & Business Media.
[146]
Matthias Jeschke, Roland Ewald, and Adelinde M. Uhrmacher. 2011. Exploring the performance of spatial stochastic simulation algorithms. Journal of Computational Physics 230, 7 (2011), 2562–2574.
[147]
Richard Jiang, Prashant Singh, Fredrik Wrede, Andreas Hellander, and Linda Petzold. 2022. Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods. PLoS Computational Biology 18, 1 (2022), 1–21.
[148]
Mathias John, Cédric Lhoussaine, Joachim Niehren, and Adelinde M. Uhrmacher. 2010. The Attributed Pi-Calculus with Priorities. Springer, Berlin, Germany, 13–76.
[149]
Einar Broch Johnsen, Reiner Hähnle, Jan Schäfer, Rudolf Schlatte, and Martin Steffen. 2011. ABS: A core language for abstract behavioral specification. In Formal Methods for Components and Objects. Lecture Notes in Computer Science, Vol. 6957. Springer, 142–164.
[150]
Einar Broch Johnsen, Rudolf Schlatte, and Silvia Lizeth Tapia Tarifa. 2012. Modeling resource-aware virtualized applications for the cloud in real-time ABS. In Formal Methods and Software Engineering. Lecture Notes in Computer Science, Vol. 7635. Springer, 71–86.
[151]
Gilles Kahn and David MacQueen. 1976. Coroutines and Networks of Parallel Processes. Research Report. Hal-Inria.
[152]
Eduard Kamburjan, Crystal Chang Din, Reiner Hähnle, and Einar Broch Johnsen. 2020. Behavioral contracts for cooperative scheduling. In Deductive Software Verification: Future Perspectives. Lecture Notes in Computer Science, Vol. 12345. Springer, 85–121.
[153]
Eduard Kamburjan, Reiner Hähnle, and Sebastian Schön. 2018. Formal modeling and analysis of railway operations with active objects. Science of Computer Programming 166 (2018), 167–193.
[154]
Eduard Kamburjan, Stefan Mitsch, and Reiner Hähnle. 2022. A hybrid programming language for formal modeling and verification of hybrid systems. Leibniz Transactions on Embedded Systems 8, 2 (2022), Article 4, 34 pages.
[155]
George-Dimitrios Kapos, Anargyros Tsadimas, Christos Kotronis, Vassilis Dalakas, Mara Nikolaidou, and Dimosthenis Anagnostopoulos. 2021. A declarative approach for transforming SysML models to executable simulation models. IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, 6 (2021), 3330–3345.
[156]
Klemens Kappel and Sebastian Jon Holmen. 2019. Why science communication, and does it work? A taxonomy of science communication aims and a survey of the empirical evidence. Frontiers in Communication 4 (2019), 55.
[157]
Daniel A. Keim, Gennady L. Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melançon. 2008. Visual analytics: Definition, process, and challenges. In Information Visualization: Human-Centered Issues and Perspectives. Lecture Notes in Computer Science, Vol. 4590. Springer, 154–175.
[158]
Tim Kelly and Rob Weaver. 2004. The goal structuring notation—A safety argument notation. In Proceedings of the Dependable Systems and Networks 2004 Workshop on Assurance Cases, Vol. 6.
[159]
Ehsan Khamespanah, Ramtin Khosravi, and Marjan Sirjani. 2018. An efficient TCTL model checking algorithm and a reduction technique for verification of timed actor models. Science of Computer Programming 153 (2018), 1–29.
[160]
Anna Klabunde, Sabine Zinn, Matthias Leuchter, and Frans Willekens. 2015. An Agent-Based Decision Model of Migration, Embedded in the Life Course-Model Description in ODD+ D Format. MPIDR Working Paper VP 2015-002. Max Planck Institute for Demographic Research.
[161]
Jack P. C. Kleijnen. 1995. Sensitivity analysis and optimization in simulation: Design of experiments and case studies. In Proceedings of the 27th Conference on Winter Simulation(WSC’95). IEEE, 133–140.
[162]
Anneke Kleppe. 2008. Software Language Engineering: Creating Domain-Specific Languages Using Metamodels. Pearson Education.
[163]
Joshua Knowles. 2006. ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation 10, 1 (2006), 50–66.
[164]
Matthias König, Holger H. Hoos, and Jan N. van Rijn. 2022. Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio. Machine Learning 111 (2022), 4565–4584.
[165]
Falko Krause, Jannis Uhlendorf, Timo Lubitz, Marvin Schulz, Edda Klipp, and Wolfram Liebermeister. 2010. Annotation and merging of SBML models with semantic SBML. Bioinformatics 26, 3 (2010), 421–422.
[166]
Justin N. Kreikemeyer and Philipp Andelfinger. 2023. Smoothing methods for automatic differentiation across conditional branches. IEEE Access 11 (2023), 143190–143211.
[167]
Marta Z. Kwiatkowska, Gethin Norman, and David Parker. 2005. Probabilistic model checking in practice: Case studies with PRISM. SIGMETRICS Performance Evaluation Review 32, 4 (2005), 16–21.
[168]
David C. Lane. 2008. The emergence and use of diagramming in system dynamics: A critical account. Systems Research and Behavioral Science 25, 1 (2008), 3–23.
[169]
Juan de Lara and Hans Vangheluwe. 2002. AToM 3: A tool for multi-formalism and meta-modelling. In Proceedings of the International Conference on Fundamental Approaches to Software Engineering. 174–188.
[170]
Averill M. Law. 2019. How to build valid and credible simulation models. In Proceedings of the 2019 Winter Simulation Conference (WSC’19). IEEE, 1402–1414.
[171]
David Leake and Joseph Kendall-Morwick. 2008. Towards case-based support for e-science workflow generation by mining provenance. In Proceedings of the European Conference on Case-Based Reasoning. 269–283.
[172]
Axel Legay, Anna Lukina, Louis Marie Traonouez, Junxing Yang, Scott A. Smolka, and Radu Grosu. 2019. Statistical model checking. In Computing and System Science. Lecture Notes in Computer Science, Vol. 10000. Springer, 478–504.
[173]
Stefan Leye, Roland Ewald, and Adelinde M. Uhrmacher. 2014. Composing problem solvers for simulation experimentation: A case study on steady state estimation. PLos One 9, 4 (2014), 1–13.
[174]
Stefan Leye, Jan Himmelspach, and Adelinde M. Uhrmacher. 2009. A discussion on experimental model validation. In Proceedings of the 2009 11th International Conference on Computer Modelling and Simulation. IEEE, 161–167.
[175]
Chen Li, Marco Donizelli, Nicolas Rodriguez, Harish Dharuri, Lukas Endler, Vijayalakshmi Chelliah, Lu Li, Enuo He, Arnaud Henry, Melanie I. Stefan, Jacky L. Snoep, Michael Hucka, Nicolas Le Novère, and Camille Laibe. 2010. BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Systems Biology 4, 1 (2010), 1–14.
[176]
Chee Sun Liew, Malcolm P. Atkinson, Michelle Galea, Tan Fong Ang, Paul Martin, and Jano I. Van Hemert. 2016. Scientific workflows: Moving across paradigms. ACM Computing Surveys 49, 4 (Dec. 2016), Article 66, 39 pages.
[177]
Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu. 2022. A survey of transformers. AI Open 3 (2022), 111–132.
[178]
Zhiyuan Jerry Lin, Raul Astudillo, Peter Frazier, and Eytan Bakshy. 2022. Preference exploration for efficient Bayesian optimization with multiple outcomes. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 4235–4258.
[179]
Jie Liu and Edward A. Lee. 2002. A component-based approach to modeling and simulating mixed-signal and hybrid systems. ACM Transactions on Modeling and Computer Simulation 12, 4 (2002), 343–368.
[180]
Catherine M. Lloyd, Matt D. B. Halstead, and Poul F. Nielsen. 2004. CellML: Its future, present and past. Progress in Biophysics and Molecular Biology 85, 2-3 (2004), 433–450.
[181]
Fabian Lorig. 2019. Hypothesis-Driven Simulation Studies: Assistance for the Systematic Design and Conducting of Computer Simulation Experiments. Springer Vieweg, Wiesbaden.
[182]
Fabian Lorig, Colja A. Becker, and Ingo J. Timm. 2017. Formal specification of hypotheses for assisting computer simulation studies. In Proceedings of the Symposium on Theory of Modeling & Simulation. 1–12.
[183]
Fabian Lorig, Emil Johansson, and Paul Davidsson. 2021. Agent-based social simulation of the COVID-19 pandemic: A systematic review. Journal of Artificial Societies and Social Simulation 24, 3 (2021), 5.
[184]
Bertram Ludäscher, Ilkay Altintas, Chad Berkley, Dan Higgins, Efrat Jaeger, Matthew Jones, Edward A. Lee, Jing Tao, and Yang Zhao. 2006. Scientific workflow management and the Kepler system. Concurrency and Computation: Practice and Experience 18, 10 (2006), 1039–1065.
[185]
Bertram Ludäscher, Shawn Bowers, and Timothy McPhillips. 2009. Scientific workflows. In Encyclopedia of Database Systems, Ling Liu and M. Tamer Özsu (Eds.). Springer US, Boston, MA, 2507–2511.
[186]
Bertram Ludäscher, Mathias Weske, Timothy M. McPhillips, and Shawn Bowers. 2009. Scientific workflows: Business as usual? In Business Process Management. Lecture Notes in Computer Science, Vol. 5701. Springer, 31–47.
[187]
Sean Luke, Claudio Cioffi-Revilla, Liviu Panait, Keith Sullivan, and Gabriel Balan. 2005. MASON: A multiagent simulation environment. SIMULATION 81, 7 (2005), 517–527.
[188]
Wolfgang Maass and Veda C. Storey. 2021. Pairing conceptual modeling with machine learning. Data & Knowledge Engineering 134 (July 2021), 101909.
[189]
Dennis G. J. C. Maneschijn, Rob H. Bemthuis, Faiza Allah Bukhsh, and Maria-Eugenia Iacob. 2022. A methodology for aligning process model abstraction levels and stakeholder needs. In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS’22). 137–147.
[190]
Shahar Maoz, Jan Oliver Ringert, and Bernhard Rumpe. 2011. CDDiff: Semantic differencing for class diagrams. In ECOOP 2011—Object-Oriented Programming, Mira Mezini (Ed.). Springer, Berlin, Germany, 230–254. https://se-rwth.de/publications/CDDiff-Semantic-Differencing-for-Class-Diagrams.pdf
[191]
Krešimir Matković, Denis Gračanin, and Helwig Hauser. 2018. Visual analytics for simulation ensembles. In Proceedings of the 2018 Winter Simulation Conference (WSC’18). 321–335.
[192]
Norm Matloff. 2008. Introduction to Discrete-Event Simulation and the Simpy Language. Department of Computer Science, University of California at Davis, Davis, CA.
[193]
Ross McNab and Fred Howell. 1996. Using Java for discrete event simulation. In Proceedings of the 12th UK Computer and Telecommunications Performance Engineering Workshop. 219–228.
[194]
Timothy M. McPhillips, Shawn Bowers, Daniel Zinn, and Bertram Ludäscher. 2009. Scientific workflow design for mere mortals. Future Generation Computer Systems 25, 5 (2009), 541–551.
[195]
Stephen J. Mellor, Kendall Scott, Axel Uhl, and Dirk Weise. 2002. Model-driven architecture. In Proceedings of the International Conference on Object-Oriented Information Systems. 290–297.
[196]
Zeeya Merali. 2010. Computational science: ...Error. Nature 467, 7317 (2010), 775–777.
[197]
Hafedh Mili, Fatma Mili, and Ali Mili. 1995. Reusing software: Issues and research directions. IEEE Transactions on Software Engineering 21, 6 (1995), 528–562.
[198]
Robin Milner, Joachim Parrow, and David Walker. 1992. A calculus of mobile processes, I. Information and Computation 100, 1 (1992), 1–40.
[199]
Eshan D. Mitra, Ryan Suderman, Joshua Colvin, Alexander Ionkov, Andrew Hu, Herbert M Sauro, Richard G. Posner, and William S. Hlavacek. 2019. PyBioNetFit and the biological property specification language. iScience 19 (2019), 1012–1036.
[200]
Parastoo Mohagheghi and Reidar Conradi. 2007. Quality, productivity and economic benefits of software reuse: A review of industrial studies. Empirical Software Engineering 12 (2007), 471–516.
[201]
Thomas Monks, Christine S. M. Currie, Bhakti Stephan Onggo, Stewart Robinson, Martin Kunc, and Simon J. E. Taylor. 2019. Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines. Journal of Simulation 13, 1 (2019), 55–67.
[202]
Luc Moreau and Paul Groth. 2013. Provenance: An Introduction to PROV. Synthesis Lectures on the Semantic Web: Theory and Technology. Morgan & Claypool.
[203]
Birgit Müller, Friedrich Bohn, Gunnar Dreßler, Jürgen Groeneveld, Christian Klassert, Romina Martin, Maja Schlüter, Jule Schulze, Hanna Weise, and Nina Schwarz. 2013. Describing human decisions in agent-based models—ODD+ D, an extension of the ODD protocol. Environmental Modelling & Software 48 (2013), 37–48.
[204]
Laura Nenzi, Ezio Bartocci, Luca Bortolussi, and Michele Loreti. 2022. A logic for monitoring dynamic networks of spatially-distributed cyber-physical systems. Logical Methods in Computer Science 18, 1 (2022), 4.
[205]
Laura Nenzi, Luca Bortolussi, Vincenzo Ciancia, Michele Loreti, and Mieke Massink. 2018. Qualitative and quantitative monitoring of spatio-temporal properties with SSTL. Logical Methods in Computer Science 14, 4 (2018), 2.
[206]
Mara Nikolaidou, George-Dimitrios Kapos, Anargyros Tsadimas, Vassilis Dalakas, and Dimosthenis Anagnostopoulos. 2015. Simulating SysML models: Overview and challenges. In Proceedings of the 2015 10th System of Systems Engineering Conference (SoSE’15).
[207]
Tanzeem Bin Noor and Hadi Hemmati. 2015. A similarity-based approach for test case prioritization using historical failure data. In Proceedings of the 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE’15). 58–68.
[208]
Joshua S. North, Christopher K. Wikle, and Erin M. Schliep. 2022. A review of data-driven discovery for dynamic systems. arXiv:2210.10663 [stat.ME] (2022).
[209]
Michael J. North, Nicholson T. Collier, Jonathan Ozik, Eric R. Tatara, Charles M. Macal, Mark Bragen, and Pam Sydelko. 2013. Complex adaptive systems modeling with Repast Simphony. Complex Adaptive Systems Modeling 1, 1 (March 2013), 3.
[210]
Michael J. North, Nicholson T. Collier, and Jerry R. Vos. 2006. Experiences creating three implementations of the repast agent modeling toolkit. ACM Transactions on Modeling and Computer Simulation 16, 1 (2006), 1–25.
[211]
Nicolas Le Novère, Andrew Finney, Michael Hucka, Upinder S. Bhalla, Fabien Campagne, Julio Collado-Vides, Edmund J. Crampin, Matt Halstead, Edda Klipp, Pedro Mendes, Poul Nielsen, Herbert Sauro, Bruce Shapiro, Jacky L. Snoep, Hugh D. Spence, and Barry L. Wanner. 2005. Minimum information requested in the annotation of biochemical models (MIRIAM). Nature Biotechnology 23, 12 (2005), 1509–1515.
[212]
Object Management Group. 2008. Meta Object Facility (MOF) 2.0 Query/View/Transformation Specification. Retrieved September 30, 2023 from https://www.omg.org/spec/QVT/1.3/PDF
[213]
Tom Oinn, Mark Greenwood, Matthew Addis, M. Nedim Alpdemir, Justin Ferris, Kevin Glover, Carole Goble, Antoon Goderis, Duncan Hull, Darren Marvin, Peter Li, Phillip Lord, Matthew R. Pocock, Martin Senger, Robert Stevens, Anil Wipat, and Chris Wroe. 2006. Taverna: Lessons in creating a workflow environment for the life sciences. Concurrency and Computation: Practice and Experience 18, 10 (2006), 1067–1100.
[214]
Kathryn Oliver, Simon Innvar, Theo Lorenc, Jenny Woodman, and James Thomas. 2014. A systematic review of barriers to and facilitators of the use of evidence by policymakers. BMC Health Services Research 14 (2014), 1–12.
[215]
Lois Orton, Ffion Lloyd-Williams, David Taylor-Robinson, Martin O’Flaherty, and Simon Capewell. 2011. The use of research evidence in public health decision making processes: Systematic review. PLoS One 6, 7 (2011), e21704.
[216]
Martin Otter, Nguyen Thuy, Daniel Bouskela, Lena Buffoni, Hilding Elmqvist, Peter Fritzson, Alfredo Garro, Audrey Jardin, Hans Olsson, Maxime Payelleville, Wladimir Schamai, Eric Thomas, and Andrea Tundis. 2015. Formal requirements modeling for simulation-based verification. In Proceedings of the 11th International Modelica Conference.
[217]
Barbara Paech and Bernhard Rumpe. 1994. A new concept of refinement used for behaviour modelling with automata. In FME’94: Industrial Benefit of Formal Methods. Lecture Notes in Computer Science, Vol. 873. Springer, 154–174.
[218]
Ernest H. Page, Laurie Litwin, Matthew T. McMahon, Brian Wickham, Mike Shadid, and Elizabeth Chang. 2012. Goal-directed grid-enabled computing for legacy simulations. In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGRID’12). IEEE, 873–879.
[219]
Ernest H. Page and Jeffrey M. Opper. 1999. Observations on the complexity of composable simulation. In Proceedings of the 31st Winter Simulation Conference. IEEE, 553–560.
[220]
Hazel R. Parry and Andrew J. Evans. 2008. A comparative analysis of parallel processing and super-individual methods for improving the computational performance of a large individual-based model. Ecological Modelling 214, 2-4 (2008), 141–152.
[221]
Ray J. Paul and Simon J. E. Taylor. 2002. What use is model reuse: Is there a crook at the end of the rainbow? In Proceedings of the Winter Simulation Conference, Vol. 1. IEEE, 648–652.
[222]
Krzysztof Pawlikowski, H.-D. J. Jeong, and J.-S. R. Lee. 2002. On credibility of simulation studies of telecommunication networks. IEEE Communications Magazine 40, 1 (2002), 132–139.
[223]
Danhua Peng, Tom Warnke, Fiete Haack, and Adelinde M. Uhrmacher. 2016. Reusing simulation experiment specifications to support developing models by successive extension. Simulation Modelling Practice and Theory 68 (2016), 33–53.
[224]
L. Felipe Perrone, Christopher S. Main, and Bryan C. Ward. 2012. SAFE: Simulation automation framework for experiments. In Proceedings of the 2012 Winter Simulation Conference (WSC’12). IEEE, 1–12.
[225]
Mikel D. Petty and Eric W. Weisel. 2019. Model composition and reuse. In Model Engineering for Simulation. Elsevier, 57–85.
[226]
Mohammad Peyman, Pedro Copado, Javier Panadero, Angel A. Juan, and Mohammad Dehghanimohammadabadi. 2021. A tutorial on how to connect Python with different simulation software to develop rich simheuristics. In Proceedings of the 2021 Winter Simulation Conference (WSC’21). 1–12.
[227]
Jan Philipps and Bernhard Rumpe. 1997. Refinement of information flow architectures. In Proceedings of the 1st IEEE International Conference on Formal Engineering Methods (ICFEM’97).
[228]
Andrew Phillips and Luca Cardelli. 2007. Efficient, correct simulation of biological processes in the stochastic pi-calculus. In Proceedings of the 2007 International Conference on Computational Methods in Systems Biology(CMSB’07). 184–199.
[229]
Michael Pidd. 2002. Simulation software and model reuse: A polemic. In Proceedings of the Winter Simulation Conference, Vol. 1. IEEE, 772–775.
[230]
Joao Felipe Pimentel, Leonardo Murta, Vanessa Braganholo, and Juliana Freire. 2017. noWorkflow: A tool for collecting, analyzing, and managing provenance from Python scripts. Proceedings of the VLDB Endowment 10, 12 (2017), 1841–1844.
[231]
Klaus Pohl, Günter Böckle, and Frank J. van der Linden. 2005. Software Product Line Engineering: Foundations, Principles and Techniques. Springer-Verlag.
[232]
Nikolas Popper, Melanie Zechmeister, Dominik Brunmeir, Claire Rippinger, Nadine Weibrecht, Christoph Urach, Martin Bicher, Günter Schneckenreither, and Andreas Rauber. 2020. Synthetic reproduction and augmentation of COVID-19 case reporting data by agent-based simulation. Data Science Journal 20 (2021), Article 16.
[233]
Corrado Priami. 1995. Stochastic \(\pi\)-calculus. Computer Journal 38, 7 (1995), 578–589.
[234]
Hazhir Rahmandad and John D. Sterman. 2012. Reporting guidelines for simulation-based research in social sciences. Systems Dynamics Review 28, 4 (2012), 396–411.
[235]
Ana Maria Ramanath and Nigel Gilbert. 2004. The design of participatory agent-based social simulations. Journal of Artificial Societies and Social Simulation 7, 4 (2004), 1. https://www.jasss.org/7/4/1.html
[236]
Ranjit Randhawa, Cliff Shaffer, and John Tyson. 2010. Model composition for macromolecular regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 7, 2 (2010), 278–287.
[237]
Oliver Reinhardt, Tom Warnke, and Adelinde M. Uhrmacher. 2022. A language for agent-based discrete-event modeling and simulation of linked lives. ACM Transactions on Modeling and Computer Simulation 32, 1 (2022), 1–26.
[238]
Judicaël Ribault and Gabriel Wainer. 2012. Using workflows and web services to manage simulation studies (WIP). In Proceedings of the 2012 Symposium on Theory of Modeling and Simulation—DEVS Integrative M&S Symposium. Article 50, 6 pages.
[239]
Stewart Robinson. 2008. Conceptual modelling for simulation part I: Definition and requirements. Journal of the Operational Research Society 59, 3 (2008), 278–290.
[240]
Stewart Robinson. 2014. Simulation: The Practice of Model Development and Use. Bloomsbury Publishing.
[241]
Stewart Robinson, Gilbert Arbez, Louis G. Birta, Andreas Tolk, and Gerd Wagner. 2015. Conceptual modeling: Definition, purpose and benefits. In Proceedings of the 2015 Winter Simulation Conference (WSC’15). 2812–2826.
[242]
Stewart Robinson, Richard E. Nance, Ray J. Paul, Michael Pidd, and Simon J. E. Taylor. 2004. Simulation model reuse: Definitions, benefits and obstacles. Simulation Modelling Practice and Theory 12, 7-8 (2004), 479–494.
[243]
Mathias Rohl and Adelinde M. Uhrmacher. 2008. Definition and analysis of composition structures for discrete-event models. In Proceedings of the 2008 Winter Simulation Conference. 942–950.
[244]
Bernhard Rumpe. 2016. Modeling with UML: Language, Concepts, Methods. Springer International. https://mbse.se-rwth.de/
[245]
Bernhard Rumpe. 2017. Agile Modeling with UML: Code Generation, Testing, Refactoring. Springer International.
[246]
Andreas Ruscheinski and Adelinde Uhrmacher. 2017. Provenance in modeling and simulation studies-bridging gaps. In Proceedings of the 2017 Winter Simulation Conference (WSC’17). IEEE, 872–883.
[247]
Andreas Ruscheinski, Tom Warnke, and Adelinde M. Uhrmacher. 2019. Artifact-based workflows for supporting simulation studies. IEEE Transactions on Knowledge and Data Engineering 32, 6 (2019), 1064–1078.
[248]
Andreas Ruscheinski, Pia Wilsdorf, Marcus Dombrowsky, and Adelinde M. Uhrmacher. 2019. Capturing and reporting provenance information of simulation studies based on an artifact-based workflow approach. In Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS’19). ACM, New York, NY, 185–196.
[249]
Andreas Ruscheinski, Pia Wilsdorf, Julius Zimmermann, Ursula van Rienen, and Adelinde M. Uhrmacher. 2022. An artefact-based workflow for finite element simulation studies. Simulation Modelling Practice and Theory 116 (2022), 102464.
[250]
Deborah A. Sadowski and Mark R. Grabau. 1999. Tips for successful practice of simulation. In Proceedings of the 1999 Winter Simulation Conference (WSC’99), Vol. 1. IEEE, 60–66.
[251]
Yohan Sahraoui, Charles De Godoy Leski, Marie-Lise Benot, Frédéric Revers, Denis Salles, Inge van Halder, Marie Barneix, and Laure Carassou. 2021. Integrating ecological networks modelling in a participatory approach for assessing impacts of planning scenarios on landscape connectivity. Landscape and Urban Planning 209 (2021), 104039.
[252]
Jan Salecker, Marco Sciaini, Katrin M. Meyer, and Kerstin Wiegand. 2019. The NLRX R package: A next-generation framework for reproducible NetLogo model analyses. Methods in Ecology and Evolution 10, 11 (2019), 1854–1863.
[253]
William H. Sanders, Tod Courtney, Daniel Deavours, David Daly, Salem Derisavi, and Vinh Lam. 2003. Multi-formalism and multi-solution-method modeling frameworks: The Möbius approach. In Proceedings of the Symposium on Performance Evaluation—Stories and Perspectives. 241–256.
[254]
Ina Schaefer, Lorenzo Bettini, Viviana Bono, Ferruccio Damiani, and Nico Tanzarella. 2010. Delta-oriented programming of software product lines. In Software Product Lines: Going Beyond. Lecture Notes in Computer Science, Vol. 6287. Springer, 77–91.
[255]
James C. Schaff, Anuradha Lakshminarayana, Robert F. Murphy, Frank T. Bergmann, Akira Funahashi, Devin P. Sullivan, and Lucian P. Smith. 2023. SBML level 3 package: Spatial processes, version 1, release 1. Journal of Integrative Bioinformatics 20, 1 (2023), 20220054.
[256]
Rudolf Schlatte, Einar Broch Johnsen, Eduard Kamburjan, and Silvia Lizeth Tapia Tarifa. 2022. The ABS simulator toolchain. Science of Computer Programming 223 (2022), 102861.
[257]
Pierre-Yves Schobbens, Patrick Heymans, and Jean-Christophe Trigaux. 2006. Feature diagrams: A survey and a formal semantics. In Proceedings of the 14th IEEE International Conference on Requirements Engineering (RE’06). IEEE, 136–145.
[258]
Hans-Jorg Schulz. 2011. Treevis.net: A tree visualization reference. IEEE Computer Graphics and Applications 31, 6 (2011), 11–15.
[259]
Marvin Schulz, Edda Klipp, Jannis Uhlendorf, and Wolfram Liebermeister. 2006. SBMLmerge, a system for combining biochemical network models. Genome Informatics 17, 1 (2006), 62–71.
[260]
Johannes Schützel, Danhua Peng, Adelinde M. Uhrmacher, and L. Felipe Perrone. 2014. Perspectives on languages for specifying simulation experiments. In Proceedings of the Winter Simulation Conference (WSC’14). IEEE, 2836–2847. http://eprints.mosi.informatik.uni-rostock.de/32/
[261]
Samuel Sepúlveda, Ania Cravero, and Cristina Cachero. 2016. Requirements modeling languages for software product lines: A systematic literature review. Information and Software Technology 69 (2016), 16–36.
[262]
Maya Retno Ayu Setyautami and Reiner Hähnle. 2021. An architectural pattern to realize multi software product lines in Java. In Proceedings of the 15th International Working Conference on Variability of Software-Intensive Systems (VaMoS’21). ACM, Article 9, 9 pages.
[263]
Gitanjali R. Shinde, Asmita B. Kalamkar, Parikshit N. Mahalle, Nilanjan Dey, Jyotismita Chaki, and Aboul Ella Hassanien. 2020. Forecasting models for coronavirus disease (COVID-19): A survey of the state-of-the-art. SN Computer Science 1, 4 (2020), 1–15.
[264]
David Shuttleworth and Jose Padilla. 2022. From narratives to conceptual models via natural language processing. In Proceedings of the 2022 Winter Simulation Conference (WSC’22). 2222–2233.
[265]
Peer-Olaf Siebers and Franziska Klügl. 2017. What software engineering has to offer to agent-based social simulation. In Simulating Social Complexity. Understanding Complex Systems Series. Springer, 81–117.
[266]
Gregory A. Silver, John A. Miller, Maria Hybinette, Gregory Baramidze, and William S. York. 2011. An ontology for discrete-event modeling and simulation. SIMULATION 87, 9 (2011), 747–773.
[267]
Marjan Sirjani. 2019. Analysing real-time distributed systems using timed actors. In Proceedings of the 23rd IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT’19). IEEE, 1.
[268]
Tijs Slaats. 2020. Declarative and hybrid process discovery: Recent advances and open challenges. Journal on Data Semantics 9, 1 (2020), 3–20.
[269]
Katherine E. Smith and Ellen Stewart. 2015. ‘Black magic’ and ‘gold dust’: The epistemic and political uses of evidence tools in public health policy making. Evidence & Policy 11, 3 (2015), 415–437.
[270]
Lucian P. Smith, Frank T. Bergmann, Alan Garny, Tomáš Helikar, Jonathan Karr, David Nickerson, Herbert Sauro, Dagmar Waltemath, and Matthias König. 2021. The simulation experiment description markup language (SED-ML): Language specification for level 1 version 4. Journal of Integrative Bioinformatics 18, 3 (2021), 20210021.
[271]
Lucian P. Smith, Erik Butterworth, James B. Bassingthwaighte, and Herbert M. Sauro. 2014. SBML and CellML translation in antimony and JSim. Bioinformatics 30, 7 (2014), 903–907.
[272]
Max Sondag, Cagatay Turkay, Kai Xu, Louise Matthews, Sibylle Mohr, and Daniel Archambault. 2022. Visual analytics of contact tracing policy simulations during an emergency response. Computer Graphics Forum 41 (2022), 29–41.
[273]
Bruno St-Aubin, Gabriel Wainer, and Fernando Loor. 2023. A survey of visualization capabilities for simulation environments. In Proceedings of the 2023 Annual Modeling and Simulation Conference (ANNSIM’23). 13–24.
[274]
Alexander Steiniger and Adelinde M. Uhrmacher. 2016. Intensional couplings in variable-structure models: An exploration based on multilevel-DEVS. ACM Transactions on Modeling and Computer Simulation 26, 2 (2016), Article 9, 27 pages.
[275]
David T. Sturrock. 2015. Tutorial: Tips for successful practice of simulation. In Proceedings of the 2015 Winter Simulation Conference (WSC’15). IEEE, 1756–1764.
[276]
David T. Sturrock. 2020. Tested success tips for simulation project excellence. In Proceedings of the 2020 Winter Simulation Conference (WSC’20). IEEE, 1143–1151.
[277]
Diana Suleimenova, Hamid Arabnejad, Wouter Edeling, and Derek Groen. 2021. Sensitivity-driven simulation development: A case study in forced migration. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 2197 (May 2021), 20200077.
[278]
Sheng Sun, Runhai Ouyang, Bochao Zhang, and Tong-Yi Zhang. 2019. Data-driven discovery of formulas by symbolic regression. MRS Bulletin 44, 7 (2019), 559–564.
[279]
Claudia Szabo and Yong Meng Teo. 2007. On syntactic composability and model reuse. In Proceedings of the 1st Asia International Conference on Modelling & Simulation (AMS’07). IEEE, 230–237.
[280]
David Taylor-Robinson, Beth Milton, Efion Lloyd-Williams, Martin O’Flaherty, and Simon Capewell. 2008. Policy-makers’ attitudes to decision support models for coronary heart disease: A qualitative study. Journal of Health Services Research & Policy 13, 4 (2008), 209–2014.
[281]
Alejandro Teran-Somohano, Alice E. Smith, Joseph Ledet, Levent Yilmaz, and Halit Oğuztüzün. 2015. A model-driven engineering approach to simulation experiment design and execution. In Proceedings of the 2015 Winter Simulation Conference (WSC’15). 2632–2643.
[282]
Thomas Thüm, Ina Schaefer, Martin Hentschel, and Sven Apel. 2012. Family-based deductive verification of software product lines. ACM SIGPLAN Notices 48, 3 (2012), 11–20.
[283]
Andreas Tolk, Saikou Y. Diallo, Jose J. Padilla, and Heber Herencia-Zapana. 2013. Reference modelling in support of M&S—Foundations and applications. Journal of Simulation 7 (2013), 69–82.
[284]
Andreas Tolk and James A. Muguira. 2003. The levels of conceptual interoperability model. In Proceedings of the 2003 Fall Simulation Interoperability Workshop, Vol. 7. Citeseer, 1–11.
[285]
Christian Tominski, James Abello, and Heidrun Schumann. 2009. CGV—An interactive graph visualization system. Computers & Graphics 33, 6 (2009), 660–678.
[286]
Mamadou K. Traoré and Alexandre Muzy. 2006. Capturing the dual relationship between simulation models and their context. Simulation Modelling Practice and Theory 14, 2 (2006), 126–142.
[287]
Gianluca Turin, Andrea Borgarelli, Simone Donetti, Einar Broch Johnsen, Silvia Lizeth Tapia Tarifa, and Ferruccio Damiani. 2020. A formal model of the Kubernetes container framework. In Leveraging Applications of Formal Methods, Verification and Validation: Verification Principles. Lecture Notes in Computer Science, Vol. 12476. Springer, 558–577.
[288]
Adelinde M. Uhrmacher, Peter Frazier, Reiner Hähnle, Franziska Klügl, Fabian Lorig, Bertram Ludäscher, Laura Nenzi, Cristina Ruiz-Martin, Bernhard Rumpe, Claudia Szabo, Gabriel A. Wainer, and Pia Wilsdorf. 2023. Context, composition, automation and communication: Towards sustainable simulation studies. In Computer Science Methods for Effective and Sustainable Simulation Studies (Dagstuhl Seminar 22401). DOI:
[289]
Andrea Unger and Heidrun Schumann. 2009. Visual support for the understanding of simulation processes. In Proceedings of the 2009 IEEE Pacific Visualization Symposium. 57–64.
[290]
Roman Vaculín, Richard Hull, Terry Heath, Craig Cochran, Anil Nigam, and Piyawadee Sukaviriya. 2011. Declarative business artifact centric modeling of decision and knowledge intensive business processes. In Proceedings of the 2011 IEEE 15th International Enterprise Distributed Object Computing Conference. IEEE, 151–160.
[291]
Priyan Vaithilingam, Tianyi Zhang, and Elena L. Glassman. 2022. Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems(CHI EA ’22). ACM, New York, NY, Article 332, 7 pages.
[292]
Anna van Bruggen, Ior Nikolic, and Jan Kwakkel. 2019. Modeling with stakeholders for transformative change. Sustainability 11, 3 (2019), 825.
[293]
Simon Van Mierlo, Bentley James Oakes, Bert Van Acker, Raheleh Eslampanah, Joachim Denil, and Hans Vangheluwe. 2020. Exploring validity frames in practice. In Systems Modelling and Management. Communications in Computer and Information Science, Vol. 1262. Springer, 131–148.
[294]
Yentl Van Tendeloo and Hans Vangheluwe. 2014. The modular architecture of the Python(P)DEVS simulation kernel. In Proceedings of the 2014 Symposium on Theory of Modeling and Simulation- (DEVS’14). 387–392.
[295]
Sandhya Vasudevan, Faizan Zafar, Yuan Xingran, Ravikumar Singh, and Wil M. P. van der Aalst. 2021. A Python extension to simulate Petri nets in process mining. arXiv preprint arXiv:2102.08774 (2021).
[296]
Jac A. M. Vennix. 1999. Group model-building: Tackling messy problems. System Dynamics Review 15, 4 (1999), 379–401.
[297]
Daniele Vernon-Bido, Andrew Collins, and John Sokolowski. 2015. Effective visualization in modeling & simulation. In Proceedings of the 48th Annual Simulation Symposium(ANSS’15). 33–40.
[298]
Alejandro F. Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer, and Julio R. Banga. 2018. Benchmarking optimization methods for parameter estimation in large kinetic models. Bioinformatics 35, 5 (2018), 830–838.
[299]
Ludovica Luisa Vissat, Michele Loreti, Laura Nenzi, Jane Hillston, and Glenn Marion. 2019. Analysis of spatio-temporal properties of stochastic systems using TSTL. ACM Transactions on Modeling and Computer Simulation 29, 4 (2019), Article 20, 24 pages.
[300]
Alexey Voinov, Karen Jenni, Steven Gray, Nagesh Kolagani, Pierre D. Glynn, Pierre Bommel, Christina Prell, Moira Zellner, Michael Paolisso, Rebecca Jordan, Eleanor Sterling, Laura Schmitt Olabisi, Philippe J. Giabbanelli, Zhanli Sun, Christophe Le Page, Sondoss Elsawah, Todd K. BenDor, Klaus Hubacek, Bethany K. Laursen, Antonie Jetter, Laura Basco-Carrera, Alison Singer, Laura Young, Jessica Brunacini, and Alex Smajgl. 2018. Tools and methods in participatory modeling: Selecting the right tool for the job. Environmental Modelling & Software 109 (2018), 232–255.
[301]
Erik Von Elm, Douglas G. Altman, Matthias Egger, Stuart J. Pocock, Peter C. Gøtzsche, and Jan P. Vandenbroucke. 2007. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. Lancet 370, 9596 (2007), 1453–1457.
[302]
Gerd Wagner. 2014. Tutorial: Information and process modeling for simulation. In Proceedings of the 2014 Winter Simulation Conference(WSC’14). IEEE, 103–117.
[303]
Gabriel Wainer, Gastón Christen, and Alejandro Dobniewski. 2001. Defining DEVS models with the CD++ toolkit. In Proceedings of SCS European Simulation Symposium.
[304]
Jyrki Wallenius, James S. Dyer, Peter C. Fishburn, Ralph E. Steuer, Stanley Zionts, and Kalyanmoy Deb. 2008. Multiple criteria decision making, multiattribute utility theory: Recent accomplishments and what lies ahead. Management Science 54, 7 (2008), 1336–1349.
[305]
Dagmar Waltemath, Richard Adams, Daniel A. Beard, Frank T. Bergmann, Upinder S. Bhalla, Randall Britten, Vijayalakshmi Chelliah, Michael T. Cooling, Jonathan Cooper, Edmund J. Crampin, Alan Garny, Stefan Hoops, Michael Hucka, Peter Hunter, Edda Klipp, Camille Laibe, Andrew K. Miller, Ion Moraru, David Nickerson, Poul Nielsen, Macha Nikolski, Sven Sahle, Herbert M. Sauro, Henning Schmidt, Jacky L. Snoep, Dominic Tolle, Olaf Wolkenhauer, and Nicolas Le Novère. 2011. Minimum information about a simulation experiment (MIASE). PLoS Computational Biology 7, 4 (2011), e1001122.
[306]
Dagmar Waltemath, Richard Adams, Frank T. Bergmann, Michael Hucka, Fedor Kolpakov, Andrew K. Miller, Ion I. Moraru, David Nickerson, Sven Sahle, Jacky L. Snoep, and Nicolas Le Novere. 2011. Reproducible computational biology experiments with SED-ML-the simulation experiment description markup language. BMC Systems Biology 5, 1 (2011), 1–10.
[307]
David Waltz and Bruce G. Buchanan. 2009. Automating science. Science 324, 5923 (2009), 43–44.
[308]
Wenguang Wang, Andreas Tolk, and Weiping Wang. 2009. The levels of conceptual interoperability model: Applying systems engineering principles to M&S. In Proceedings of the 2009 Spring Simulation Multiconference(SpringSim’09). Article 168, 9 pages.
[309]
Tom Warnke, Oliver Reinhardt, and Adelinde M. Uhrmacher. 2016. Population-based CTMCS and agent-based models. In Proceedings of the 2016 Winter Simulation Conference (WSC’16). IEEE, 1253–1264.
[310]
Tom Warnke and Adelinde M. Uhrmacher. 2018. Complex simulation experiments made easy. In Proceedings of the 2018 Winter Simulation Conference (WSC’18). IEEE, 410–424.
[311]
Tim Weilkiens. 2008. Systems Engineering with SysML/UML: Modeling, Analysis, Design. Elsevier.
[312]
Meike Will, Gunnar Dressler, David Kreuer, Hans-Hermann Thulke, Adrienne Grêt-Regamey, and Birgit Müller. 2021. How to make socio-environmental modelling more useful to support policy and management? People and Nature 3, 3 (2021), 560–572.
[313]
Pia Wilsdorf, Nadine Fischer, Fiete Haack, and Adelinde M. Uhrmacher. 2021. Exploiting provenance and ontologies in supporting best practices for simulation experiments: A case study on sensitivity analysis. In Proceedings of the 2021 Winter Simulation Conference (WSC’21). 1–12.
[314]
Pia Wilsdorf, Fiete Haack, and Adelinde M. Uhrmacher. 2020. Conceptual models in simulation studies: Making it explicit. In Proceedings of the 2020 Winter Simulation Conference (WSC’20). IEEE, 2353–2364.
[315]
Pia Wilsdorf, Jakob Heller, Kai Budde, Julius Zimmermann, Tom Warnke, Christian Haubelt, Dirk Timmermann, Ursula van Rienen, and Adelinde M. Uhrmacher. 2022. A model-driven approach for conducting simulation experiments. Applied Sciences 12, 16 (2022), 7977.
[316]
Pia Wilsdorf, Anja Wolpers, Jason Hilton, Fiete Haack, and Adelinde Uhrmacher. 2023. Automatic reuse, adaption, and execution of simulation experiments via provenance patterns. ACM Transactions on Modeling and Computer Simulation 33, 1-2 (Feb. 2023), Article 4, 27 pages.
[317]
Pia Wilsdorf, Marian Zuska, Philipp Andelfinger, and Adelinde M. Uhrmacher. 2023. Validation without data—Formalized stylized facts of time series. In Proceedings of the Winter Simulation Conference (WSC’23). IEEE, 2674–2685.
[318]
Eric Winsberg. 2010. Science in the Age of Computer Simulation. University of Chicago Press.
[319]
Eric Winsberg, Jason Brennan, and Chris W. Surprenant. 2020. How government leaders violated their epistemic duties during the SARS-CoV-2 crisis. Kennedy Institute of Ethics Journal 30, 3 (2020), 215–242.
[320]
Marcus Woo. 2020. The rise of no/low code software development—No experience needed? Engineering (Beijing, China) 6, 9 (2020), 960.
[321]
Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, and David Eckhoff. 2020. OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms. Concurrency and Computation: Practice and Experience 32, 21 (2020), e5807.
[322]
Bernhard P. Zeigler. 1976. Theory of Modeling and Simulation. John Wiley.
[323]
Bernard P. Zeigler. 1977. Constructs for the specifications of models and experimental frames. ACM SIGSIM Simulation Digest 9, 1 (Sept. 1977), 12–13.
[324]
Bernard P. Zeigler, Alexandre Muzy, and Ernesto Kofman. 2018. Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations. Academic Press, San Diego, CA.
[325]
Bernard P. Zeigler, Herbert Praehofer, and Tag Gon Kim. 2000. Theory of Modelling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. Academic Press, San Diego, CA.
[326]
Bernard P. Zeigler and Hessam Sarjoughian. 2005. Introduction to DEVS Modeling and Simulation with JAVA: Developing Component-Based Simulation Models. Technical Report. Arizona Center of Integrative Modeling and Simulation, University of Arizona.
[327]
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter. 2018. Towards automated deep learning: Efficient joint neural architecture and hyperparameter search. arXiv:1807.06906 (2018).
[328]
Feng Zhu, Yiping Yao, Jin Li, and Wenjie Tang. 2019. Reusability and composability analysis for an agent-based hierarchical modelling and simulation framework. Simulation Modelling Practice and Theory 90 (2019), 81–97.
[329]
Daniel Zinn, Shawn Bowers, Timothy M. McPhillips, and Bertram Ludäscher. 2009. Scientific workflow design with data assembly lines. In Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Scienc (WORKS’09). ACM, New York, NY.
[330]
Steffen Zschaler, Dimitrios S. Kolovos, Nikolaos Drivalos, Richard F. Paige, and Awais Rashid. 2009. Domain-specific metamodelling languages for software language engineering. In Proceedings of the International Conference on Software Language Engineering. 334–353.
[331]
Steffen Zschaler and Fiona A. C. Polack. 2020. A family of languages for trustworthy agent-based simulation. In Proceedings of the 13th ACM SIGPLAN International Conference on Software Language Engineering (SLE’20). ACM, New York, NY, 16–21.

Cited By

View all

Index Terms

  1. Context, Composition, Automation, and Communication: The C2AC Roadmap for Modeling and Simulation

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Modeling and Computer Simulation
    ACM Transactions on Modeling and Computer Simulation  Volume 34, Issue 4
    October 2024
    231 pages
    EISSN:1558-1195
    DOI:10.1145/3613727
    • Editor:
    • Wentong Cai
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 August 2024
    Online AM: 19 June 2024
    Accepted: 03 June 2024
    Revised: 13 May 2024
    Received: 07 October 2023
    Published in TOMACS Volume 34, Issue 4

    Check for updates

    Author Tags

    1. Modeling
    2. simulation
    3. state of the art
    4. open challenges
    5. reuse
    6. composition
    7. communication
    8. reproducibility
    9. automation
    10. intelligent modeling and simulation lifecycle

    Qualifiers

    • Research-article

    Funding Sources

    • German Research Foundation (DFG)
    • NSERC–Canada
    • Wallenberg AI, Autonomous Systems and Software Program—Humanities
    • Society (WASP-HS), which was funded by the Marianne and Marcus Wallenberg Foundation and the Marcus and Amalia Wallenberg Foundation

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)467
    • Downloads (Last 6 weeks)58
    Reflects downloads up to 08 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    Full Text

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media