Modeling and simulation (M&S) is the use of models – physical, mathematical, or otherwise logicalrepresentation of a system, entity, phenomenon, or process – as a basis for simulations – methods for implementing a model (either statically or) over time – to develop data as a basis for managerial or technical decision making through the exercise of simulation governance which covers analysis, experimentation, and training. As such, M&S can facilitate understanding a system's behavior without actually testing the system in the real world. For instance, to determine which type of spoiler would improve traction the most while designing a race car, a computer simulation of the car could be used to estimate the effect of different spoiler shapes on the coefficient of friction in a turn. Useful insights about different decisions in the design could be gleaned without actually building the car. In addition, simulation can support experimentation that occurs totally in software, or in human-in-the-loop environments where simulation represents systems or generates data needed to meet experiment objectives. Furthermore, simulation can be used to train persons using a virtual environment that would otherwise be difficult or expensive to produce.
The use of M&S within engineering is well recognized. Simulation technology belongs to the tool set of engineers of all application domains and has been included in the body of knowledge of engineering management. M&S helps to reduce costs, increase the quality of products and systems, and document and archive lessons learned.
M&S is a discipline on its own. Its many application domains often lead to the assumption that M&S is pure application. This is not the case and needs to be recognized by engineering management experts who want to use M&S. To ensure that the results of simulation are applicable to the real world, the engineering manager must understand the assumptions, conceptualizations, and implementation constraints of this emerging field.
Interest in simulations
Technically, simulation is well accepted. The 2006 National Science Foundation (NSF) Report on "Simulation-based Engineering Science" showed the potential of using simulation technology and methods to revolutionize the engineering science. Among the reasons for the steadily increasing interest in simulation applications are the following:
- Using simulations is generally cheaper, safer and sometimes more ethical than conducting real-world experiments. For example, supercomputers are sometimes used to simulate the detonation of nuclear devices and their effects in order to support better preparedness in the event of a nuclear explosion. Similar efforts are conducted to simulate hurricanes and other natural catastrophes.
See also: Hurricane Weather Research and Forecasting model
- Simulations can often be even more realistic than traditional experiments, as they allow the free configuration of environment parameters found in the operational application field of the final product. Examples are supporting deep water operation of the US Navy or the simulating the surface of neighbored planets in preparation of NASA missions.
- Simulations can often be conducted faster than real time. This allows using them for efficient if-then-else analyses of different alternatives, in particular when the necessary data to initialize the simulation can easily be obtained from operational data. This use of simulation adds decision support simulation systems to the tool box of traditional decision support systems.
- Simulations allow setting up a coherent synthetic environment that allows for integration of simulated systems in the early analysis phase via mixed virtual systems with first prototypical components to a virtual test environment for the final system. If managed correctly, the environment can be migrated from the development and test domain to the training and education domain in follow-on life cycle phases for the systems (including the option to train and optimize a virtual twin of the real system under realistic constraints even before first components are being built).
The military and defense domain, in particular within the United States, has been the main M&S champion, in form of funding as well as application of M&S. E.g., M&S in modern military organizations is part of the acquisition/procurement strategy. Specifically, M&S is used to conduct Events and Experiments that influence requirements and training for military systems. As such, M&S is considered an integral part of systems engineering of military systems. Other application domains, however, are currently catching up. M&S in the fields of medicine, transportation, and other industries is poised to rapidly outstrip DoD's use of M&S in the years ahead, if it hasn't already happened.
Simulation in science
Further information: Scientific modelling
Modeling and simulation is important in research. Representing the real systems either via physical reproductions at smaller scale, or via mathematical models that allow representing the dynamics of the system via simulation, allows exploring system behavior in an articulated way which is often either not possible, or too risky in the real world.
As an emerging discipline
"The emerging discipline of M&S is based on developments in diverse computer science areas as well as influenced by developments in Systems Theory, Systems Engineering, Software Engineering, Artificial Intelligence, and more. This foundation is as diverse as that of engineering management and brings elements of art, engineering, and science together in a complex and unique way that requires domain experts to enable appropriate decisions when it comes to application or development of M&S technology in the context of this paper. The diversity and application-oriented nature of this new discipline sometimes results in the challenge, that the supported application domains themselves already have vocabularies in place that are not necessarily aligned between disjunctive domains. A comprehensive and concise representation of concepts, terms, and activities is needed that make up a professional Body of Knowledge for the M&S discipline. Due to the broad variety of contributors, this process is still ongoing."
Padilla et al. recommend in "Do we Need M&S Science" to distinguish between M&S Science, Engineering, and Applications.
- M&S Science contributes to the Theory of M&S, defining the academic foundations of the discipline.
- M&S Engineering is rooted in Theory but looks for applicable solution patterns. The focus is general methods that can be applied in various problem domains.
- M&S Applications solve real world problems by focusing on solutions using M&S. Often, the solution results from applying a method, but many solutions are very problem domain specific and are derived from problem domain expertise and not from any general M&S theory or method.
Models can be composed of different units (models at finer granularity) linked to achieve a specific goal; for this reason they can be also called modelling solutions.
More generally, modeling and simulation is a key enabler for systems engineering activities as the system representation in a computer readable (and possibly executable) model enables engineers to reproduce the system (or Systems of System) behavior. A collection of applicative modeling and simulation method to support systems engineering activities in provided in.
In pharmacy education
The shortage of pharmacists in the United States has prompted increases in class sizes and the number of satellite and distance-learning programs at colleges and schools of pharmacy. This rapid expansion has created a burden on existing clinical experimental sites. The Accreditation Council on Pharmacy Education (ACPE) requires at least 1440 hours of advanced pharmacy practice experience (APPE); included among the 1440 hours of APPE, the ACPE requires colleges and schools of pharmacy to provide a minimum of 300 hours of introductory pharmacy practice experience (IPPE) interspersed throughout the first three years of the pharmacy curriculum. Simulation training may be one such model to provide students with the opportunity to apply didactic knowledge and reduce the burden on experiential sites.The inclusion of simulation in IPPEs has gained acceptance and is encouraged by ACPE as described in the Policies and Procedures for ACPE Accreditation of Professional Degree Programs – January 2010. Addendum 1.3, Simulations for Introductory Pharmacy Practices Experiences – Approved June 2010, states:
Simulation may not be utilized to supplant or replace the minimum expectation for time spent in actual pharmacy practice settings as set forth in the previously established policy. Beyond the majority of time in actual pharmacy practice settings, colleges and schools may utilize simulation to account for no greater than 20% (e.g., 60 hours of a 300-hour IPPE program) of total IPPE time.
Several pharmacy colleges and schools have incorporated simulation as part of their core curricula. At the University of Pittsburgh School of Pharmacy, high-fidelity patient simulators are used to reinforce therapeutics. While the University of Rhode Island College of Pharmacy integrated their simulation program into their pharmacology and medicinal chemistry coursework; and was the first college of pharmacy to purchase a high-fidelity patient simulator. Some pharmacy colleges and schools host virtual reality and full environment simulation programs. For example, Purdue University School of Pharmacy and the university's Envision Center for Data Perceptualization collaborated with the United States Pharmacopeia (USP) to create a virtual clean room that is USP 797 standards compliant.
There are many categorizations possible, but the following taxonomy has been very successfully used in the defense domain, and is currently applied to medical simulation and transportation simulation as well.
- Analyses Support is conducted in support of planning and experimentation. Very often, the search for an optimal solution that shall be implemented is driving these efforts. What-if analyses of alternatives fall into this category as well. This style of work is often accomplished by simulysts - those having skills in both simulation and as analysts. This blending of simulation and analyst is well noted in Kleijnen.
- Systems Engineering Support is applied for the procurement, development, and testing of systems. This support can start in early phases and include topics like executable system architectures, and it can support testing by providing a virtual environment in which tests are conducted. This style of work is often accomplished by engineers and architects.
- Training and Education Support provides simulators, virtual training environments, and serious games to train and educate people. This style of work is often accomplished by trainers working in concert with computer scientists.
A special use of Analyses Support is applied to ongoing business operations. Traditionally, decision support systems provide this functionality. Simulation systems improve their functionality by adding the dynamic element and allow to compute estimates and predictions, including optimization and what-if analyses.
Further information: Conceptual model
Although the terms "modeling" and "simulation" are often used as synonyms within disciplines applying M&S exclusively as a tool, within the discipline of M&S both are treated as individual and equally important concepts. modeling is understood as the purposeful abstraction of reality, resulting in the formal specification of a conceptualization and underlying assumptions and constraints. M&S is in particular interested in models that are used to support the implementation of an executable version on a computer. The execution of a model over time is understood as the simulation. While modeling targets the conceptualization, simulation challenges mainly focus on implementation, in other words, modeling resides on the abstraction level, whereas simulation resides on the implementation level.
Conceptualization and implementation – modeling and simulation – are two activities that are mutually dependent, but can nonetheless be conducted by separate individuals. Management and engineering knowledge and guidelines are needed to ensure that they are well connected. Like an engineering management professional in systems engineering needs to make sure that the systems design captured in a systems architecture is aligned with the systems development, this task needs to be conducted with the same level of professionalism for the model that has to be implemented as well. As the role of big data and analytics continues to grow, the role of combined simulation of analysis is the realm of yet another professional called a simulyst – in order to blend algorithmic and analytic techniques through visualizations available directly to decision makers. A study designed for the Bureau of Labor and Statistics by Lee et al. provides an interesting look at how bootstrap techniques (statistical analysis) were used with simulation to generate population data where there existed none.
Modeling and simulation has only recently become an academic discipline of its own. Formerly, those working in the field usually had a background in engineering.
The following institutions offer degrees in Modeling and Simulation:
- Ph D. Programs
- Masters Programs
- National University of Science and Technology, Pakistan (Islamabad, Pakistan)
- Arizona State University (Tempe, AZ)
- Old Dominion University (Norfolk, VA)
- University of Central Florida (Orlando, FL)
- University of Alabama in Huntsville (Huntsville, AL)
- Middle East Technical University (Ankara, Turkey)
- University of New South Wales (Australia)
- Naval Postgraduate School (Monterey, CA)
- Center for Modeling and Simulation (M.Tech (Modelling & Simulation)) (Savitribai Phule Pune University, India)
- Columbus State University (Columbus, GA)
- Purdue University Calumet (Hammond, IN)
- Delft University of Technology (Delft, The Netherlands)
- Professional Science Masters Programs
- Graduate Certificate Programs
- Undergraduate Programs
Modeling and Simulation Body of Knowledge
The Modeling and Simulation Body of Knowledge (M&S BoK) is the domain of knowledge (information) and capability (competency) that identifies the modeling and simulation community of practice and the M&S profession, industry and market.
The M&S BoK Index is a set of pointers providing handles so that subject information content can be denoted, identified, accessed, and manipulated.
Three activities have to be conducted and orchestrated to ensure success:
- a model must be produced that captures formally the conceptualization,
- a simulation must implement this model, and
- management must ensure that model and simulation are interconnected and on the current state (which means that normally the model needs to be updated in case the simulation is changed as well).
- ^"Department of Defense INSTRUCTION NUMBER 5000.61: Modeling and Simulation (M&S) Verification, Validation, and Accreditation"(PDF). Department of Defense. 2009-12-09.
- ^"Department of Defense DIRECTIVE NUMBER 5000.59: DoD Modeling and Simulation (M&S) Management"(PDF). Department of Defense. 2007-08-08.
- ^"Report on Simulation-Based Engineering Science"(PDF). National Science Foundation (NSF) Blue Ribbon Panel. 2006-05-01.
- ^George Dvorsky. "Supercomputer simulates nuclear explosion down to the molecular level". io9. Gawker Media.
- ^"Hurricane Force Supercomputing: Petascale Simulations of Sandy". HPCwire.
- ^"NOAA's SciJinks :: Simulate a Hurricane".
- ^"9 Super-Cool Uses for Supercomputers". LiveScience.com.
- ^Collins, A.J.; S.R. Shefrey; J. Sokolowski; C.D. Turnitsa; E. Weisel (January 2011). "Modeling and Simulation Standards Study: Healthcare Workshop report". VMASC Report, Suffolk VA.
- ^Tolk, Andreas. "Engineering Management Challenges for Applying Simulation as a Green Technology"(PDF).
- ^Padilla, Jose; S.Y. Diallo; A. Tolk (October 2011). "Do We Need M&S Science?"(PDF). SCS M&S Magazine (4): 161–166. Retrieved July 1, 2012.
- ^Gianni, Daniele; D'Ambrogio, Andrea; Tolk, Andreas, eds. (December 2, 2014). Modeling and Simulation-Based Systems Engineering Handbook (1st ed.). CRC Press. p. 513. ISBN 9781466571457.
- ^Vyas, D., Wombwell, E., Russell, E., & Caligiuri, F. (2010). High-fidelity patient simulationseries to supplement introductory pharmacy practice experiences. Am J Pharm Educ, 74(9), 169.
- ^"Accreditation Council forPharmacy Education: Policies and Procedures for ACPE Accreditation forProfessional Degree Programs"(PDF). 2011. Retrieved July 13, 2013.
- ^Lin, K., Travlos, D. V., Wadelin, J. W., & Vlasses, P. H. (2011). Simulation and IntroductoryPharmacy Practice Experiences. AmericanJournal of Pharmaceutical Education, 75(10), 209. doi: 10.5688/ajpe7510209
- ^Lee, Hyunshik J.; et al. (2013). "Simulation Study to Validate Sample Allocation for the National Compensation Survey"(PDF). JSM 2013 - Survey Research Methods Section. Bureau of Labor Statistics.
- ^ abWaite, W. (2004) "Foundations '04: A Workshop for VV&A in the 21st Century, Session 10: V&V Education Initiatives
- The Springer Publishing House publishes the Simulation Foundations, Methods and Applications Series .
- Recently, Wiley started their own Series on Modeling and Simulation .
One thing we come across quite often when discussing our ideas about modern tech education is the confusion between computer science and software engineering.
Whether we look at studies describing the digital skill shortage in the workforce and the consequences for our economy, at job descriptions from employers in search of ICT professionals or at politicians demanding more and better educational programs aimed at digital competences – in most cases there is no clear definition of the skills profile in question. ICT Professionals, Developers, Programmers, Software Engineers, Computer Scientists – all too often are they used as synonyms.
If Europe needs 825.000 ICT professionals until 2020, does it mean everybody should study computer science?
Of course not.
Computer science is about taking complex problems and deriving a solution from math, science and computational theory.David Budden in “Degrees Demystified”
Computer Scientists are first and foremost scientists. They possess a deep knowledge of the theoretical foundations in mathematics and information science and can develop complex algorithms and advance scientific research. They operate in a world of rigorous analyses, clearly defined concepts and proven facts.
The digital skills in demand as described by employers, labor market studies and politicians are of a different kind. They involve the ability to interact with human beings and to create easy to use software solutions for real world problems with limited resources in a highly unreliable and dynamically changing environment.
David Budden describes the difference in his analysis as follows:
Where computer science is about taking complex problems and deriving a solution from mathematics, science and computational theory, software engineering is very much focused around designing, developing and documenting beautiful, complete, user-friendly software.
Chuck Connell uses the following analogy in his article “Software Engineering ≠ Computer Science“:
Imagine a brilliant structural engineer who is the world’s expert on building materials, stress and strain, load distributions, wind shear, earthquake forces, etc. Architects in every country keep this person on their speed-dial for every design and construction project. Would this mythical structural engineer necessarily be good at designing the buildings he or she is analyzing? Not at all. Our structural engineer might be lousy at talking to clients, unable to design spaces that people like to inhabit, dull at imagining solutions to new problems, and boring aesthetically. Structural engineering is useful to physical architects, but is not enough for good design. Successful architecture includes creativity, vision, multi-disciplinary thinking, and humanity.
As does successful software engineering.
Why is this distinction so important?
- Because it helps to choose a study program that fits one’s abilities: Many have what it takes to become a successful software developer but lack the mathematical interest or ability to succeed in computer science. We cannot afford to discourage these young talents from choosing a career in software engineering, especially because – as Sarah Mei lays out in her article “Programming is not math”: “Learning to program is more like learning a new language than it is like doing math problems. And the experience of programming today, in industry, is more about language than it is about math.”
- Because it helps to choose a study program that meets expectations: Starting computer science studies to become a software developer is probably going to be disappointing, because Computer Science is more a “degree in applied mathematics” than a “degree where you learn how to code”, as David Budden puts it. The dropout rates in computer science programs (at some German universities as high as 40%) are a depressing monument to this confusion.
- Because it helps politicians and institutions to identify the approaches and instruments that improve tech education and contribute to closing the digital skills gap.
- Because it helps employers to better understand where to look for future employees that support their growth and successfully drive the digital transformation.
- Because it helps us understand how to design a study program that produces graduates with competence profiles that enable them to become successful software developers and that meet the demands of future employers.
Software engineering is very much focused around designing, developing and documenting beautiful, complete, user-friendly software.David Budden in “Degrees Demystified”
We are not trying to diminish the importance of computer science as a discipline or computer scientists as a driving force of digital innovation and advancement in scientific research. But the vast majority of the 800.000 digital professionals missing in the European labor market in the year 2020 do not have the competence profile of a computer science major. They need to be creative problem solvers with communication and soft skills and the ability to utilize scientific innovations to make a difference in real life.
A note about Germany: While the education system in English-speaking countries at least offers the distinction between computer science and software engineering, the German education system almost exclusively talks about “Informatik” (information science) meaning the science of systematic information processing. There are variations like “Angewandte Informatik” (applied information science), “Technische Informatik” (technical information science) or “Medieninformatik” (media information science), but the starting point of any discussion in this field is Informatik. Due to a strong dual education system (combining an apprenticeship in a company with vocational training at a vocational school) the role of German universities was traditionally focussed on scientific education while looking down on the idea of teaching hands-on knowledge and skills with practical relevance with regard to future employers. As a consequence the need for a software engineering study program as alternative to information science is even greater in Germany (as this commentator elaborates).
In our next post we will take a look at the reaction of the education industry to the existing demand for software engineers: the staggering amount and perceived success of coding bootcamps.
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