Relevant bibliographies by topics / Modeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 July 2025

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Journal articles on the topic "Modeling"

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A. Razak, Amir. "Overview of Wind Turbine Modeling in Modelica Language." International Journal of Engineering and Technology 4, no. 5 (2012): 551–53. http://dx.doi.org/10.7763/ijet.2012.v4.430.

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Mitra, Novelyn L., Ma Jobelle R. David, and Rommel Pariñas Deus Gleena P. Pascual. "Predictive Modeling for Criminology Licensure Examination Success Through Mathematical Modelling." International Journal of Research Publication and Reviews 5, no. 3 (March 2, 2024): 168–76. http://dx.doi.org/10.55248/gengpi.5.0324.0604.

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Muller, Pierre-Alain, Frédéric Fondement, Benoît Baudry, and Benoît Combemale. "Modeling modeling modeling." Software & Systems Modeling 11, no. 3 (August 4, 2010): 347–59. http://dx.doi.org/10.1007/s10270-010-0172-x.

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Jha, Ritambhara. "Statistical Modeling vs. Machine Learning Modeling: A Comparative Research." International Journal of Science and Research (IJSR) 10, no. 6 (June 5, 2021): 1793–95. http://dx.doi.org/10.21275/sr24203181719.

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Killeen, Peter R. "MODELING MODELING." Journal of the Experimental Analysis of Behavior 71, no. 2 (March 1999): 275–80. http://dx.doi.org/10.1901/jeab.1999.71-275.

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Khamzina, B. E., A. S. Kudussov, and Zh Sh Kuralbayeva. "Modeling in CupCarbon." Bulletin of the Karaganda University. Pedagogy series 107, no. 3 (September 29, 2022): 22–27. http://dx.doi.org/10.31489/2022ped3/22-27.

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Modeling is one of the key categories of modern cognitive theory and design-engineering practice. Simulation and visualization of real physical processes and phenomena are important technological tools and simulation results. The article presents a comparative review and analysis of existing emulators, such as Cisco Packet Tracer and UNetLab, as well as the rationale for choosing CupCarbon as a platform for modeling, in particular, wireless networks for the Internet of Things. The functional potential of today’s most common emulators like Cisco Packet Tracer and UNetLab are significant. Cisco Packet Tracer is a powerful software emulator, which gives the possibility to users to simulate networks, organizing them with the almost unlimited number of devices, using the equipment and adjusting it for the specific tasks of this or that environment. The program gives the opportunity to develop the quality of decision-making speed, creativity and critical thinking, configuration and troubleshooting networks with the use of virtual equipment and simulated connection. Despite the great advantages of this software emulator, it has several disadvantages, which make it incorrect for modeling IoT networks, such as the lack of simulation in a 2D/3D environment and adding urban and natural noise, not a complete emulation of IOS, almost everything that goes beyond the CCNA, it also cannot be built on it, the possible manifestation of a variety of bugs that are treated only by restarting the program.
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Dimanova, Donika. "THEORY OF MODELING." Journal scientific and applied research 4, no. 1 (October 10, 2013): 106–11. http://dx.doi.org/10.46687/jsar.v4i1.87.

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The aim of this study is to provide a brief introduction to the theory of modeling, to classify the models and methods of modeling, to explore the most common tools for modeling and simulation. This is necessary to specify the main characteristics on which to compare the qualities of the means of modeling and simulation and to compare these tools based on specified characteristics. Results of the study can be applied to using simulation tools (the choice of situational model ) for crisis management.
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Winkler, Istvan, Eugene N. Sokolov, John A. Spinks, Risto Naatanen, and Heikki Lyytinen. "Modeling the Modeling." American Journal of Psychology 116, no. 2 (2003): 336. http://dx.doi.org/10.2307/1423590.

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Laloë, Francis. "Modelling sustainability: from applied to involved modeling." Social Science Information 46, no. 1 (March 2007): 87–107. http://dx.doi.org/10.1177/0539018407073659.

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English Models are used as tools for studying sustainable development. They are a priori neutral, but this neutrality can be upset if the analysis of observations done to answer questions in reference to a given a priori definition of sustainability leads to the identification of sustainability contexts that do not correspond to the initial definition. The practice of actors may for example involve substitution between elements of a natural capital, which may not be in agreement with a strong definition of sustainability. Therefore we may have to consider the need for transition from a prescriptive to a descriptive approach. The context of modelling depends on the object we want to be preserved (what we observe). If this object is a resource-exploitation system, the perception of sustainability comes from observation of the system. The observation process may have been initiated by a mono-disciplinary question, but, sustainability being dependent on an object which must be described, no unique definition can be proposed. The definition and perception of sustainability are themselves processes to which modelling sciences may have to contribute. French Des modèles sont utilisés comme outils dans le contexte des études sur le développement durable. Ils sont a priori neutres, mais cette neutralité peut être mise à mal si des analyses d'observations réalisées pour répondre à des questions posées en référence à une définition donnée de durabilité conduisent à identifier des contextes de durabilité qui ne correspondent pas nécessairement à la définition initialement adoptée. La pratique d'acteurs peut ainsi par exemple reposer sur des substitutions entre éléments d'un capital naturel, ce qui peut ne pas correspondre à une définition très forte de la durabilité. Dans ces conditions on peut discuter du passage d'une approche prescriptive à une approche descriptive. La question du choix d'un contexte de modélisation dépend de l'objet que l'on veut préserver (ce que l'on observe). S'il s'agit d'un système d'exploitation d'une ressource, la perception de la durabilité découle de l'observation du système. Le processus d'observation a pu être initié selon une définition a priori associée à un questionnement souvent "mono-disciplinaire", mais la durabilité étant inféodée à un objet qu'il faut représenter, on ne peut en proposer une définition unique. La définition et la perception de la durabilité sont des processus auxquels les sciences de modélisation peuvent être amenées à contribuer
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Tyson, Stephen, and Joost Herweijer. "Knowledge management and 3D modelling: overview and application to iterative 3D modelling workflows." APPEA Journal 51, no. 2 (2011): 683. http://dx.doi.org/10.1071/aj10063.

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As 3D reservoir modeling is effectively the centre stage in many multi-disciplinary reservoir management efforts, the need for effective knowledge management is paramount to ensure: Inclusion of pre-existing knowledge is in the model, including absent (or controversial knowledge), reflected in uncertainties. A definition of a modeling process comprehensively covers the reservoirs issues identified while leading to fit for purpose results in a given imposed time span. Storage of knowledge generated and a modeling audit trail during the modeling process to ensure adequate and efficient model updates over time. Creation of a link between the integrated modeling process and the discipline processes that generate the basic data that underpin the model Current 3D modelling methods and tools allow for creation of models and storing model specific workflows (i.e. software related parameters and processing chains). In addition, various efforts are undertaken to store modelling best practice, which typically entails general know-how about creating models. During a specific 3D modelling project, however, a considerable body of knowledge is generated about what and what not makes the model work for a given reservoir. Such knowledge, which are essential for systematic uncertainty assessment during an existing modelling effort or subsequent modelling efforts, needs to be managed in order to retain its relevance. This extended abstract focuses on application of knowledge management in the EP industry, particularly on how process or workflow-based knowledge management approaches add value in the context of 3D modeling projects for multidisciplinary reservoir management.
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Dissertations / Theses on the topic "Modeling"

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SOUSA, HENRIQUE PRADO. "INTEGRATING INTENTIONAL MODELING TO PROCESS MODELING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=19928@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>A modelagem de processos de negócio é utilizada por empresas que desejam documentar detalhes do fluxo de execução de seus processos, resultando em um documento rico em detalhes sobre o negócio. Este artefato também é utilizado pela Engenharia de Software para elicitação de requisitos de sistema. A modelagem intencional possui foco na modelagem de objetivos - definidos como metas e metas flexíveis - e registra as estratégias que podem ser seguidas por um ator de forma a melhor atender suas necessidades, mapeando tarefas e recursos necessários, além disso, também aborda as dependências entre atores. É importante que os modelos de processos de negócio estejam alinhados aos objetivos da organização de forma a prover fonte de informações confiável que gere consequentemente requisitos alinhados ao negócio. Diversas ferramentas estão disponíveis no mercado com o objetivo de apoiar a modelagem dos processos de negócio e dos objetivos organizacionais, entretanto, percebe-se que as soluções disponíveis ainda são incompletas quando se fala na integração de modelos de processos e modelo de objetivos e formas de verificação do alinhamento entre processos e objetivos organizacionais a partir da modelagem. Na arquitetura organizacional, processos de negócio e objetivos são intrinsecamente interdependentes, porém, as linguagens de modelagem atuais não oferecem recursos suficientes para tratar processos e objetivos de forma alinhada, uma vez que existem deficiências na integração entre a camada de modelagem de objetivos e a de processos. Assim, o uso do ferramental disponível que se apoia nessas linguagens e métodos dificulta sobremaneira a tarefa de identificar se os processos utilizados para gerar serviços e produtos, verdadeiramente atingem os objetivos da organização, bem como o impacto que as mudanças nos objetivos causariam nos processos de negócio. Neste trabalho integramos uma linguagem de modelagem de objetivos a uma linguagem de processos de negócio e provemos os elementos e métodos necessários para ampliar a capacidade de análise do alinhamento dos processos de negócio às estratégias organizacionais.<br>The business processes modeling is used by companies who wish to document details of the execution flow of their processes, resulting in a document rich in details about the business. This artifact is also used by the Software Engineering for system requirements elicitation. The intentional modeling is focused on objectives - defined as goals and softgoals - and registers the strategies that may be followed by an actor in a way to better meet their needs, mapping the tasks and resources needs, in addition, it also addresses the dependencies between actors. It is important that business processes models are aligned to the objectives of the organization in order to provide reliable information source that generates consequently requirements aligned to business. Several tools are available on the market in order to support the business processes and organizational objectives modeling, however, it’s possible to realize that the available solutions are still incomplete when it comes to the integration of process models and goals models and ways to check the alignment between organizational goals and processes using the models. In the organizational architecture, business processes and goals are intrinsically interdependent, however, the current modeling languages treat process and goals in a misaligned way, since there are deficiencies in the integration between the modeling layer of objectives and processes. Thus, the use of the available tools that supports these language and methods greatly complicates the task of identify if the processes used to generate products and services truly achieve the organizational goals as well as the impact of the changes in the goals would cause in business processes. In this paper we integrated a goal modeling language to a business processes modeling language and proved the elements and methods needed to expand the capacity of analysis of the alignment between the business processes and the organizational strategies.
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Hansen, Daniel L. "Modeling." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27137.

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Queiroz, Eurico Tiago Justino. "Modelling Benguela niños using the regional oceanic modeling system (ROMS)." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/6499.

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Includes bibliographical references (leaves 132-141).<br>Pierre Florenchie<br>This study is framed by three questions: firstly, could the Regional Oceanic Modelling System (ROMS) reproduce the seasonal cycle of the equatorial Atlantic? Secondly, what is the nature of the link between remote forcing in the western equatorial Atlantic and Benguela Niños/Niñas? Thirdly, what is the impact of these events on the equatorial Atlantic Ocean SST and circulation patterns? The results obtained suggest that the model is very sensitive to different wind stress forcing, particularly in respect of the impact on the mixed layer characteristics. As a result the equatorial upwelling is overestimated in both temporal and spatial scales.
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Andersson, Conny. "Design of the Modelica Library VehProLib with Non-ideal Gas Models in Engines." Thesis, Linköpings universitet, Fordonssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121817.

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This thesis covers the reconstruction and the redesign of the modeling library VehProLib,which is constructed in the modeling language Modelica with help of the modeling toolWolfram SystemModeler. The design choices are discussed and implemented. This thesisalso includes the implementation of a turbocharger package and an initial study of the justificationof the ideal gas law in vehicle modeling. The study is made with help of Van derWaals equation of states as a reference of non-ideal gas model. It will be shown that for themean-value-engine-model, the usage of ideal gas law is justified.
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Milligan, Walter W. Jr. "Deformation modeling and constitutive modeling for anisotropic superalloys." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/19922.

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Musunuri, Shravana Kumar. "Hybrid electric vehicle modeling in generic modeling environment." Master's thesis, Mississippi State : Mississippi State University, 2006. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.

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Stollhoff, Rainer. "Modeling Prosopagnosia." Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-39600.

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Prosopagnosia is defined as a profound deficit in facial identification which can be either acquired due to brain damage or is present from birth, i.e. congenital. Normally, faces and objects are processed in different parts of the inferotemporal cortex by distinct cortical systems for face vs. object recognition, an association of function and location. Accordingly, in acquired prosopagnosia locally restricted damage can lead to specific deficits in face recognition. However, in congenital prosopagnosia faces and objects are also processed in spatially separated areas. Accordingly, the face recognition deficit in congenital prosopagnosia can not be solely explained by the association of function and location. Rather, this observation raises the question why and how such an association evolves at all. So far, no quantitative or computational model of congenital prosopagnosia has been proposed and models of acquired prosopagnosia have focused on changes in the information processing taking place after in icting some kind of \damage" to the system. To model congenital prosopagnosia, it is thus necessary to understand how face processing in congenital prosopagnosia differs from normal face processing, how differences in neuroanatomical development can give rise to differences in processing and last but not least why facial identification requires a specialized cortical processing system in the first place. In this work, a computational model of congenital prosopagnosia is derived from formal considerations, implemented in artificial neural network models of facial information encoding, and tested in experiments with prosopagnosic subjects. The main hypothesis is that the deficit in congenital prosopagnosia is caused by a failure to obtain adequate descriptions of individual faces: A predisposition towards a reduced structural connectivity in visual cortical areas enforces descriptions of visual stimuli that lack the amount of detail necessary to distinguish a specific exemplar from its population, i.e. achieve a successful identification. Formally recognition tasks can be divided into identification tasks (separating a single individual from its sampling population) and classification tasks (partitioning the full object space into distinct classes). It is shown that a high-dimensionality in the sensory representation facilitates individuation (\blessing of dimensionality"), but complicates estimation of object class representations (\curse of dimensionality"). The dimensionality of representations is then studied explicitly in a neural network model of facial encoding. Whereas optimal encoding entails a \holistic" (high-dimensional) representation, a constraint on the network connectivity induces a decomposition of faces into localized, \featural" (low-dimensional) parts. In an experimental validation, the perceptual deficit in congenital prosopagnosia was limited to holistic face manipulations and didn't extend to featural manipulations. Finally, an extensive and detailed investigation of face and object recognition in congenital prosopagnosia enabled a better behavioral characterization and the identification of subtypes of the deficit. In contrast to previous models of prosopagnosia, here the developmental aspect of congenital prosopagnosia is incorporated explicitly into the model, quantitative arguments for a deficit that is task specific (identification) - and not necessarily domain specific (faces) - are provided for synthetic as well as real data (face images), and the model is validated empirically in experiments with prosopagnosic subjects.
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Дядечко, Алла Миколаївна, Алла Николаевна Дядечко, Alla Mykolaivna Diadechko, and V. O. Hlushchenko. "Computer modeling." Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/13473.

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Akhlagi, Ali. "A Modelica-based framework for modeling and optimization of microgrids." Thesis, KTH, Energiteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263037.

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Microgrids have lately drawn much attention due to their considerable financial benefits and the increasing concerns about environmental issues. A solution that can address different engineering problems - from design to operation - is desired for practical reasons and to ensure consistency of the analyses. In this thesis, the capabilities of a Modelicabased framework is investigated for various microgrid optimization problems. Various sizing and scheduling problems are successfully formulated and optimized using nonlinear and physical component models, covering both electrical and thermal domains. Another focus of the thesis is to test the optimization platform when varying the problem formulation; performance and robustness tests have been performed with different boundary conditions and system setups. The results show that the technology can effectively handle complex scheduling strategies such as Model Predictive Control and Demand Charge Management. In sizing problems, although the platform can efficiently size the components while simultaneously solving for the economical load dispatch for short horizons (weekly or monthly), the implemented approach would require adaptations to become efficient on longer horizons (yearly).
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Boström, Rikard, and Lars-Olof Moilanen. "Capacity profiling modeling for baseband applications." Thesis, Karlstad University, Faculty of Economic Sciences, Communication and IT, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-3352.

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<p>Real-time systems are systems which must produce a result within a given time frame. A result given outside of this time frame is as useless as not delivering any result at all. It is therefore essential to verify that real-time systems fulfill their timing requirements. A model of the system can facilitate the verification process. This thesis investigates two possible methods for modeling a real-time system with respect to CPU-utilization and latency of the different components in the system. The two methods are evaluated and one method is chosen for implementation.The studied system is the decoder of a WCDMA system which utilizes a real-time operating called system OSEck. The methodology of analyzing the system and different ways of obtaining measurements to base the model upon will be described. The model was implemented using the simulation library VirtualTime, which contains a model of the previously mentioned operating system. Much work was spent acquiring input for the model, since the quality of the model depends largely on the quality of the analysis work. The model created contains two of the studied systems main components.This thesis identifies thorough system knowledge and efficient profiling methods as the key success factors when creating models of real-time systems.</p>
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Books on the topic "Modeling"

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Tiller, Michael. Introduction to Physical Modeling with Modelica. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1561-6.

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O'Reilly, Susie. Modeling. New York: Thomson Learning, 1993.

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Cantwell, Lois. Modeling. New York: F. Watts, 1986.

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Lesh, Richard, Peter L. Galbraith, Christopher R. Haines, and Andrew Hurford, eds. Modeling Students' Mathematical Modeling Competencies. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6271-8.

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Lesh, Richard, Peter L. Galbraith, Christopher R. Haines, and Andrew Hurford, eds. Modeling Students' Mathematical Modeling Competencies. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-0561-1.

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McDuffie, Amy Roth, ed. Mathematical Modeling and Modeling Mathematics. Reston, VA: National Council of Teachers of Mathematics, 2016.

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D, Johnson Peter, ed. Clay modelling for everyone: Pottery, sculpture and miniatures without a wheel. Tunbridge Wells: Search Press, 1991.

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Cugini, Umberto, and Michael Wozny, eds. From Geometric Modeling to Shape Modeling. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35495-8.

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Bergé, Jean-Michel. Meta-Modeling: Performance and Information Modeling. Boston, MA: Springer US, 1996.

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Jean-Michel, Bergé, Levia Oz, and Rouillard Jacques, eds. Meta-modeling: Performance and information modeling. Dordrecht: Kluwer Academic Publishers, 1996.

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Book chapters on the topic "Modeling"

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Muller, Pierre-Alain, Frédéric Fondement, and Benoît Baudry. "Modeling Modeling." In Model Driven Engineering Languages and Systems, 2–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04425-0_2.

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Lutz, Herb. "UF Modeling (Ultrafiltration Modeling)." In Encyclopedia of Membranes, 1943–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-44324-8_1208.

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Jordan, Gregory. "Modeling." In Practical Neo4j, 23–37. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0022-3_3.

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Figueras, Joan. "Modeling." In Low Power Design in Deep Submicron Electronics, 81–104. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5685-5_4.

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Oono, Yoshitsugu. "Modeling." In The Nonlinear World, 191–233. Tokyo: Springer Japan, 2012. http://dx.doi.org/10.1007/978-4-431-54029-8_4.

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Bestaoui Sebbane, Yasmina. "Modeling." In Lighter than Air Robots, 7–44. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-2663-5_2.

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Torbert, Shane. "Modeling." In Applied Computer Science, 171–200. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1888-7_7.

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Kopetz, Hermann. "Modeling." In Simplicity is Complex, 37–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20411-2_4.

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Verschuur, Gerrit L. "Modeling." In Interstellar Matters, 158–64. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4522-3_15.

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Li, Tatsien, Yongji Tan, Zhijie Cai, Wei Chen, and Jingnong Wang. "Modeling." In SpringerBriefs in Mathematics, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41425-1_1.

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Conference papers on the topic "Modeling"

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Tiller, Michael, Cleon Davis, Hubertus Tummescheit, and Nizar Trigui. "Powertrain Modeling With Modelica." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2352.

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Abstract In this paper we will describe the development of models for prediction of powertrain performance. Our goal is to develop a library of components to model combustion, gas dynamics and mechanical response. We will also demonstrate the ease with which we can replace traditional component models (e.g., mechanically actuated valves) with non-traditional component models (e.g., electro-mechanically actuated valves) without having to change or reformulate any of the other components in our system. The models were developed using the Modelica modeling language (Modelica Design Group, 1999) which allows component-based descriptions of behavior for complex engineering systems. Modelica is particularly well suited for creating behavioral models that are typical for powertrain plant models In addition to writing component models, the freely available Modelica Standard Library contains basic models from various engineering disciplines (e.g., resistors, shafts, springs). With this approach, models can be symbolically preprocessed to improve computational performance. In addition, code can be generated from the Modelica model which can be used as either a stand alone analysis tool, imported into Simulink as an S-function or downloaded for use in real-time hardware in the loop experiments.
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Batteh, John, Jesse Gohl, James Ferri, Quang Le, Bill Glandorf, Bob Sherman, and Rudolfs Opmanis. "Material Production Process Modeling with Automated Modelica Models from IBM Rational Rhapsody." In American Modelica Conference 2022, Dallas, October 26-28. Linköping University Electronic Press, 2023. http://dx.doi.org/10.3384/ecp21186158.

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This paper describes a method to author dynamic simulation models in Modelica from a manufacturing architectural model structure in SysML. Modelica models are generated from IBM Rational Rhapsody and simulated using Modelon Impact. Following a brief overview of the overall modeling approach and tool coupling, the Modelica modeling is detailed for computing process throughput and processing time. Following the model overview, a sample application for the production of the pharmaceutical ingredient atropine is demonstrated.
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Baharev, Ali, and Arnold Neumaier. "Chemical Process Modeling in Modelica." In 9th International MODELICA Conference, Munich, Germany. Linköping University Electronic Press, 2012. http://dx.doi.org/10.3384/ecp12076955.

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Radil, Lukas, Petr Mastny, and Jan Machacek. "Modeling Vanadium Redox Battery in Modelica." In 2014 15th International Scientific Conference on Electric Power Engineering (EPE). IEEE, 2014. http://dx.doi.org/10.1109/epe.2014.6839505.

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Hailiang Zhang, Zhuoping Yu, Zaimin Zhong, Bonian Wu, and Xinbo Chen. "Modelica based modeling of automotive transmission." In 2014 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific). IEEE, 2014. http://dx.doi.org/10.1109/itec-ap.2014.6940686.

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Drogies, Stefan, and Michael Bauer. "Modeling Road Vehicle Dynamics with Modelica." In SAE 2002 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2002. http://dx.doi.org/10.4271/2002-01-1219.

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Binder, William, Christiaan Paredis, and Humberto Garcia. "Hybrid Energy System Modeling in Modelica." In the 10th International Modelica Conference, March 10-12, 2014, Lund, Sweden. Linköping University Electronic Press, 2014. http://dx.doi.org/10.3384/ecp14096979.

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Kuric, Muhamed, Nedim Osmic, and Adnan Tahirovic. "Multirotor Aerial Vehicle modeling in Modelica." In The 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017. Linköping University Electronic Press, 2017. http://dx.doi.org/10.3384/ecp17132373.

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Qasem, Montaser A., Ali Dhaw Aljadi, and Ezzedin Muftah Enbiah. "Modeling Directional Overcurrent Relays using Modelica." In 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). IEEE, 2023. http://dx.doi.org/10.1109/mi-sta57575.2023.10169427.

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Tiller, Michael M. "Modeling Supply and Demand in Modelica." In The 13th International Modelica Conference, Regensburg, Germany, March 4–6, 2019. Linköing University Electronic Press, 2019. http://dx.doi.org/10.3384/ecp19157365.

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Reports on the topic "Modeling"

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Holtzman, Lilliane, Thomas Stanton, and Maureen Psaila-Dombrowski. RCWG Modeling Symposium - Matrix Modeling &amp; Risk Modeling Overview. Office of Scientific and Technical Information (OSTI), May 2025. https://doi.org/10.2172/2568635.

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Naylor, Bruce, David Ress, and Risto Miikkulainen. Neurometric Modeling: Computational Modeling of Individual Brains. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada547370.

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Fortner, Jeff, Scott Painter, Vineet Kumar, and Riley Cumberland. GDSA - Modeling and Integration – ORNL: PFLOTRAN modeling. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1909111.

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Wahid, Shahriar, Susan Cuddy, Aditya Bastola, Arun Shrestha, and Auro Almeida. Gender equality, disability and social inclusion in water modelling: A practitioners’ toolkit. International Centre for Integrated Mountain Development (ICIMOD), December 2024. https://doi.org/10.53055/icimod.1070.

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Abstract:
The toolkit consists of two modules that guide modeling practices or training delivery, including recommendations for training format, duration, and breaks. It is organized to support typical water modeling processes and includes practical guides, tips for further learning, examples, and practice exercises for individuals or groups to achieve GEDSI in water modeling. The toolkit aims to help users: Understand the benefits of incorporating Gender Equality, Disability, and Social Inclusion (GEDSI) into water modelling, recognizing its value in addressing complex water management challenges. Comprehend the negative impacts of GEDSI-blind modelling, which can perpetuate disparities and biases in water management. Build confidence in integrating GEDSI considerations into modeling practices by acquiring the necessary skills and knowledge. Identify strategic GEDSI entry points in the modeling process, such as data collection, analysis, interpretation, model development, and scenario modeling. Develop proficiency in gathering and analyzing GEDSI-disaggregated data and using GEDSI-aware modeling techniques. Gain skills to develop and model scenarios that promote GEDSI and evaluate their potential impacts. The primary audience includes hydrologists, water resources engineers, water managers, and policymakers who use modeling extensively. Other stakeholders, such as those in GEDSI, social science, community representation, agriculture, and disaster risk management, may also find the toolkit useful.
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Acemoglu, Daron, and Pascual Restrepo. Modeling Automation. Cambridge, MA: National Bureau of Economic Research, February 2018. http://dx.doi.org/10.3386/w24321.

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Hengartner, Nicolas W. Predictive modeling. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1092430.

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Gupta, Pradeep K. Tribological Modeling. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada372092.

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Laganelli, A. L., and S. M. Dash. Turbulence Modeling. Fort Belvoir, VA: Defense Technical Information Center, October 1991. http://dx.doi.org/10.21236/ada415956.

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Christiano, Lawrence, Martin Eichenbaum, and Charles Evans. Modeling Money. Cambridge, MA: National Bureau of Economic Research, January 1998. http://dx.doi.org/10.3386/w6371.

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Plimpton, Steven James, Julieanne Heffernan, Darryl Yoshio Sasaki, Amalie Lucile Frischknecht, Mark Jackson Stevens, and Laura J. Douglas Frink. Modeling biomembranes. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/875627.

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