Готові списки джерел за темами / Models and simulation

Добірка наукової літератури з теми "Models and simulation"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Models and simulation"

1

Dixion-Gough, Robert. "Theoretical Models and Simulation Models." Géographes associés 18, no. 1 (1996): 51–53. http://dx.doi.org/10.3406/geoas.1996.2006.

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2

Glenn, Floyd. "The Case for Micro-models." Proceedings of the Human Factors Society Annual Meeting 33, no. 18 (October 1989): 1228–32. http://dx.doi.org/10.1177/154193128903301813.

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This paper examines the appropriate role of human performance micro-models in simulations of human-machine system operations. Requirements for general human micro-models are considered relative to the objectives of simulation studies, the conditions under which simulations are constructed and used, the status of human performance data bases and models, and the features provided with general-purpose simulation software. This investigation focuses particularly on a new simulation tool for simulating human-machine systems; it is known as the Human Operator Simulator – Version V (HOS-V). A general design principle of HOS-V has been to provide embedded human performance micro-models for the basic performance processes that seem most pervasive and interactive with other processes. These include representations for processes of body movement, cognition, and attention. Key to these representations are the substructures in each area. Body movement models describe locations of body parts and constraints on their movement. Cognition models describe how the human processes information through perception, memory, decision-making, and action initiation. The attention model describes how a limited attentional resource is allocated to the various body movement and cognition processes, each of which has a defined attentional requirement. Plans for implementation of micro-model components of HOS-V are discussed.
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3

Bot, G. P. A. "GREENHOUSE SIMULATION MODELS." Acta Horticulturae, no. 245 (August 1989): 315–25. http://dx.doi.org/10.17660/actahortic.1989.245.42.

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4

Kelly, Drew, and Betsy M. Nolan. "Models and Simulation." Journal of Bone and Joint Surgery 98, no. 5 (March 2016): e21. http://dx.doi.org/10.2106/jbjs.15.01178.

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5

Choucri, Nazli, and Christopher Heye. "3.5. Simulation models." Energy 15, no. 3-4 (March 1990): 363–78. http://dx.doi.org/10.1016/0360-5442(90)90096-k.

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6

Gray, Jeff, and Bernhard Rumpe. "Models in simulation." Software & Systems Modeling 15, no. 3 (July 2016): 605–7. http://dx.doi.org/10.1007/s10270-016-0544-y.

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7

Rozinat, A., R. S. Mans, M. Song, and W. M. P. van der Aalst. "Discovering simulation models." Information Systems 34, no. 3 (May 2009): 305–27. http://dx.doi.org/10.1016/j.is.2008.09.002.

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8

Lankford, Philip M. "Testing Simulation Models." Geographical Analysis 6, no. 3 (September 3, 2010): 295–302. http://dx.doi.org/10.1111/j.1538-4632.1974.tb00514.x.

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9

Thompson, James. "Forward simulation models." WIREs Computational Statistics 2, no. 1 (January 2010): 61–68. http://dx.doi.org/10.1002/wics.68.

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10

Ritchie, Nicholas W. M. "Spectrum Simulation in DTSA-II." Microscopy and Microanalysis 15, no. 5 (September 16, 2009): 454–68. http://dx.doi.org/10.1017/s1431927609990407.

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AbstractSpectrum simulation is a useful practical and pedagogical tool. Particularly with complex samples or trace constituents, a simulation can help to understand the limits of the technique and the instrument parameters for the optimal measurement. DTSA-II, software for electron probe microanalysis, provides both easy to use and flexible tools for simulating common and less common sample geometries and materials. Analytical models based on ϕ(ρz) curves provide quick simulations of simple samples. Monte Carlo models based on electron and X-ray transport provide more sophisticated models of arbitrarily complex samples. DTSA-II provides a broad range of simulation tools in a framework with many different interchangeable physical models. In addition, DTSA-II provides tools for visualizing, comparing, manipulating, and quantifying simulated and measured spectra.
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Дисертації з теми "Models and simulation"

1

Ozhan, Gurkan. "Transforming Mission Space Models To Executable Simulation Models." Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613826/index.pdf.

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This thesis presents a two step automatic transformation of Field Artillery Mission Space Conceptual Models (ACMs) into High Level Architecture (HLA) Federation Architecture Models (FAMs) into executable distributed simulation code. The approach followed in the course of this thesis adheres to the Model-Driven Engineering (MDE) philosophy. Both ACMs and FAMs are formally defined conforming to their metamodels, ACMM and FAMM, respectively. ACMM is comprised of a behavioral component, based on Live Sequence Charts (LSCs), and a data component based on UML class diagrams. Using ACMM, the Adjustment Followed by Fire For Effect (AdjFFE) mission, which serves as the source model for the model transformation case study, is constructed. The ACM to FAM transformation, which is defined over metamodel-level graph patterns, is carried out with the Graph Rewriting and Transformation (GReAT) tool. Code generation from a FAM is accomplished by employing a model interpreter that produces Java/AspectJ code. The resulting code can then be executed on an HLA Run-Time Infrastructure (RTI). Bringing a fully fledged transformation approach to conceptual modeling is a distinguishing feature of this thesis. This thesis also aims to bring the chart notations to the attention of the mission space modeling community regarding the description of military tasks, particularly their communication aspect. With the experience gained, a set of guidelines for a domainindependent transformer from any metamodel-based conceptual model to FAM is offered.
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2

Hauth, Michael. "Visual simulation of deformable models." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=97232125X.

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3

Kreula, Juha. "Quantum simulation of fermionic models." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:1de91b0b-1cd7-4a95-ac56-312529b51357.

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This work is a theoretical study of fermionic models. We focus on problems where highly controllable quantum simulators of these models have an important role, and we utilise both the analogue and the digital paradigm of quantum simulation. In the part on analogue quantum simulation, we focus on the proposed 'spin-asymmetric' Josephson effect where Cooper-paired spins display frequency synchronized Josephson oscillations with spin-dependent amplitudes. We consider different scenarios where the phenomenon could manifest in ultracold atomic Fermi gases. We study a Fermi gas Josephson junction in the recently realized Josephson plasma oscillation regime with an additional spin-dependent potential and show that the asymmetry in the resulting spin-dependent plasma oscillation amplitudes is on the order of a couple of per cent. We also demonstrate numerically that spin-asymmetric Josephson-like currents occur in a one-dimensional spin-dependent optical superlattice, with amplitude asymmetries up to 39%. Finally, we show that at zero temperature the tunable critical current in ferromagnetic Josephson junctions can be explained by the spin-asymmetric Josephson effect. In the part where digital quantum simulation is used, we propose a hybrid quantum-classical approach to studying strongly correlated fermion models. In this approach, a digital quantum simulator works in conjunction with a classical feedback loop to solve the infinite-dimensional Hubbard model directly in the thermodynamic limit. The scheme implements the well-established dynamical mean-field theory (DMFT) method, such that the digital quantum simulator solves the classically hard DMFT impurity problem and self-consistency is taken care of in a classical computer. We first present a few-qubit proof-of-principle setup for equilibrium systems that implements the simplified 'two-site' DMFT. This few-qubit setup is used for a qualitative description of the Mott transition in the half-filled infinite-dimensional Hubbard model. We then describe a scalable setup for simulating non-equilibrium many-body quantum dynamics by proposing the implementation of the non-equilibrium extension of DMFT with the hybrid device.
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4

Pohl, Thomas. "Design of adaptable simulation models." Thesis, Sheffield Hallam University, 2006. http://shura.shu.ac.uk/20240/.

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In today's world, with ever increasing competition, modelling and simulation proves to be a very helpful tool. Many methodologies exist to help build a simulation model from scratch. In terms of adaptability, most current attempts focus on either the operational side, ie the automated integration of data into a model, or the creation of new software. However, very few attempts are being made to improve the adaptability of shelved models built in existing simulation software. As a result, there is a certain reluctance, in some areas, to use simulation to its full potential. Based on these facts, it is obvious that anything, which makes reuse of simulation models easier, can help improve the use and spread of simulation as a valuable tool to maintain a company's competitiveness. In order to find such a solution, the following issues are looked at in this thesis: The changes to a simulation model that constitute the biggest problem, ways to minimise those changes, and possibilities to simplify the implementation of those changes. Those factors are evaluated, first by investigating current practices of building adaptable simulation models via a literature review, then the most difficult changes to implement in a simulation model, and the most frequent types of simulation software, are identified by means of interviews and questionnaire surveys. Next, parameters describing the adaptability of a simulation model are defined. In a further step, two of the most widely used simulation packages are benchmarked against a variety of tasks, reflecting the changes most frequent to models. The benchmarking study also serves to define and test certain elements regarding their suitability for adaptable models. Based on all those steps, model building guidelines for the creation of adaptable simulation models are developed and then validated by means of interviews and a framed field experiment. The interviews and questionnaire reveal that deleting is the easiest task and modifying the most complicated, while handling devices are the most difficult element to modify. The results also show that simulators (eg Arena) are the most widespread type of simulation software. The benchmarking showed that Arena is overall more adaptable than Simul8, and confirms the findings from the user survey. Also, it shows that sequencing is very helpful for modifying models, while the use of sub-models decrease the adaptability. Finally, the validation proves that the model building guidelines substantially increase the adaptability of models.
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5

Bezuidenhoudt, Cecile Margaret. "Optimisation of complex simulation models." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/6572.

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Computer simulation models are widely and frequently used to model real systems to predict output responses under specified input conditions. Choosing optimal simulation parameters leads to improved operation of the model but it is still a challenge as to how to go about optimally selecting these parameter values. The aim of this thesis was to see if a method could be found to optimise a simulation model provided by a client. This thesis provides a review of the literature of various simulation optimisation techniques that exist. Five of these simulation optimisation techniques - Simulated Annealing, Genetic Algorithms, Nested Partitions, Ordinal Optimisation and the Nelson-Matejcik Method - were selected and applied to a test case stochastic simulation model to gain an understanding into the techniques for their use in optimising the test model. These techniques were then used and applied to optimise a real life simulation model provided by a client. A technique combining the Ordinal Optimisation and Simulated Annealing optimisation methods provided the best results. This technique was provided to the client as a strategy to implement into their simulation model.
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6

O'Keefe, R. M. "Developing simulation models : an interpreter for visual active simulation." Thesis, University of Southampton, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.236359.

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7

Bertelrud, P. Anders I. "An object-oriented simulator for the visual simulation environment." Thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-08142009-040521/.

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8

Buchta, Christian, and Sara Dolnicar. "Learning by simulation. Computer simulations for strategic marketing decision support in tourism." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/1718/1/document.pdf.

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This paper describes the use of corporate decision and strategy simulations as a decision-support instrument under varying market conditions in the tourism industry. It goes on to illustrate this use of simulations with an experiment which investigates how successful different market segmentation approaches are in destination management. The experiment assumes a competitive environment and various cycle-length conditions with regard to budget and strategic planning. Computer simulations prove to be a useful management tool, allowing customized experiments which provide insight into the functioning of the market and therefore represent an interesting tool for managerial decision support. The main drawback is the initial setup of a customized computer simulation, which is time-consuming and involves defining parameters with great care in order to represent the actual market environment and to avoid excessive complexity in testing cause-effect-relationships. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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9

Kajero, Olumayowa T. "Meta-model assisted calibration of computational fluid dynamics simulation models." Thesis, University of Surrey, 2017. http://epubs.surrey.ac.uk/813857/.

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Computational fluid dynamics (CFD) is a computer-based analysis of the dynamics of fluid flow, and it is widely used in chemical and process engineering applications. However, computation usually becomes a herculean task when calibration of the CFD models with experimental data or sensitivity analysis of the output relative to the inputs is required. This is due to the simulation process being highly computationally intensive, often requiring a large number of simulation runs, with a single simulation run taking hours or days to be completed. Hence, in this research project, the kriging meta-modelling method was coupled with expected improvement (EI) global optimisation approach to address the CFD model calibration challenge. In addition, a kriging meta-model based sensitivity analysis technique was implemented to study the model parameter input-output relationship. A novel EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution, where existing normal distribution-based EI measures are not applicable. This novel EI measure suggested the values of CFD model parameters to simulate with, hence minimising SSE and improving the match between simulation and experiments. To test the proposed methodology, a non-CFD numerical simulation case of the semi-batch reactor was considered as a case study which confirmed a saving in computational time, and an improvement of the simulation model with the actual plant data. The usefulness of the developed method has been subsequently demonstrated through a CFD case study of a single-phase flow in both a straight type and convergent-divergent type annular jet pump, where both a single turbulent model parameter, C_μ and two turbulent model parameters, C_μ and C_2ε where considered for calibration. Sensitivity analysis was subsequently based on C_μ as the input parameter. In calibration using both single and two model parameters, a significant improvement in the agreement with experimental data was obtained. The novel method gave a significant reduction in simulation computational time as compared to traditional CFD. A new correlation was proposed relating C_μ to the flow ratio, which could serve as a guide for future simulations. The meta-model based calibration aids exploration of different parameter combinations which would have been computationally challenging using CFD. In addition, computational time was significantly reduced with kriging-assisted sensitivity analysis studies which explored effect of different C_μ values on the output, the pressure coefficient. The numerical simulation case of the semi-batch reactor was also used as a basis of comparison between the previous EI measure and the newly proposed EI measure, which overall revealed that the latter gave a significant improvement at fewer number of simulation runs as compared to the former. The research studies carried out has hence been able to propose and successfully demonstrate the use of a novel methodology for faster calibration and sensitivity analysis studies of computational fluid dynamics simulations. This is essential in the design, analysis and optimisation of chemical and process engineering systems.
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10

Cumin, David. "Simulation in anaesthesia: standards and models." Thesis, University of Auckland, 2010. http://hdl.handle.net/2292/7163.

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Harm from medical errors costs tens of billions of dollars and causes tens of thousands of deaths each year in the United States alone. This is not so surprising considering the lack of opportunity for medical students and consultants alike to practice rare events, to be systematically exposed to common scenarios, or to be objectively assessed. Similarly, there are limited opportunities to test new medical devices or procedures without putting patients at harm. Simulation has provided the airline industry, in particular, with such opportunities and is a contributing factor to the safety of air travel. Simulation in medicine has the same potential but there are few, if any, concrete standards to adhere to. My objectives were to provide a structure for such standards to be set, to develop methods for evaluating the modelled physiology of simulators, and to further the mathematical modelling needed for autonomous, realistic, and extendable simulators. To these ends, I have analysed the key components of simulation and reviewed existing simulators and the modelling which underpins their responses to interventions. A framework for standards was developed with a focus on the physiological modelling of anaesthetic simulators. Methods for evaluating the repeatability and concordance of simulators were explored using simple interventions. I created an extendable database of accurate, complete physiological and interventional time series data from anaesthetic cases. Some of these cases were used to confirm the repeatability and concordance results, and then used to develop more advanced methods for evaluating fidelity. Finally, I used a novel modelling approach to create an integrated model of the human cardio respiratory system encompassing cellular through to systemic physiological processes which produced promising results. It is my hope that the work in this thesis may pave the way for more realistic simulators and a more standardised approach to simulation so that medical errors are reduced.
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Книги з теми "Models and simulation"

1

Chorafas, Dimitris N. Financial Models and Simulation. London: Palgrave Macmillan UK, 1995. http://dx.doi.org/10.1057/9780230374836.

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2

N, Chorafas Dimitris. Financial models and simulation. New York, N.Y: St. Martins, 1995.

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3

Chorafas, Dimitris N. Financial models and simulation. Basingstoke: Macmillan, 1995.

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4

Pumain, Denise, and Romain Reuillon. Urban Dynamics and Simulation Models. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46497-8.

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5

Cai, Jianping, Feng Lin, and Hock Soon Seah. Graphical Simulation of Deformable Models. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-51031-6.

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6

Lantuéjoul, Christian. Geostatistical simulation: Models and algorithms. Berlin: Springer, 2002.

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7

Geostatistical simulation: Models and algorithms. New York: Springer, 2002.

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8

Mann, Jakob. Models in micrometeorology. Roskilde: Risø National Laboratory, 1994.

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9

Modeling and simulation. Wellesley, MA: A.K. Peters, 1994.

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10

Arora, Narain. MOSFET Models for VLSI Circuit Simulation. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-9247-4.

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Частини книг з теми "Models and simulation"

1

Ihde, Don. "Models, Models Everywhere." In Simulation, 79–86. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-5375-4_5.

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2

Morgan, Michael M., MacDonald J. Christie, Luis De Lecea, Jason C. G. Halford, Josee E. Leysen, Warren H. Meck, Catalin V. Buhusi, et al. "Simulation Models." In Encyclopedia of Psychopharmacology, 1238. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_855.

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3

Volino, Pascal, and Nadia Magnenat-Thalmann. "Simulation Models." In Virtual Clothing, 11–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57278-4_2.

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4

Cesari, Giovanni, John Aquilina, Niels Charpillon, Zlatko Filipović, Gordon Lee, and Ion Manda. "Simulation Models." In Modelling, Pricing, and Hedging Counterparty Credit Exposure, 45–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04454-0_3.

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Adelman, Irma. "Simulation Models." In The New Palgrave Dictionary of Economics, 1–4. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1057/978-1-349-95121-5_1333-1.

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Sobolev, Boris, Victor Sanchez, and Lisa Kuramoto. "Simulation Models." In Health Care Evaluation Using Computer Simulation, 53–62. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2233-4_4.

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Adelman, Irma. "Simulation Models." In The New Palgrave Dictionary of Economics, 12357–60. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_1333.

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Fox, William P., and Robert E. Burks. "Simulation Models." In Advanced Mathematical Modeling with Technology, 391–439. First edition. | Boca Raton : C&H\CRC Press, 2021. | Series: Advances in applied mathematics: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003046196-9.

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Adelman, Irma. "Simulation Models." In Econometrics, 225–28. London: Palgrave Macmillan UK, 1990. http://dx.doi.org/10.1007/978-1-349-20570-7_30.

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Browne, Jim. "Simulation and Simulation Models." In Computer-Aided Production Management, 123–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73318-5_8.

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Тези доповідей конференцій з теми "Models and simulation"

1

Cambeiro, Joao, Julien Deantoni, and Vasco Amaral. "Supporting the Engineering of Multi-Fidelity Simulation Units With Simulation Goals." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00053.

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2

Gomes, Claudio, Levi Lucio, and Hans Vangheluwe. "Semantics of Co-simulation Algorithms with Simulator Contracts." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2019. http://dx.doi.org/10.1109/models-c.2019.00124.

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"The Reference Model Models Clinicaltrials.Gov." In 2017 Summer Simulation Multi-Conference. Society for Modeling and Simulation International (SCS), 2017. http://dx.doi.org/10.22360/summersim.2017.scsc.022.

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4

Cremona, Fabio, Marten Lohstroh, David Broman, Edward A. Lee, Michael Masin, and Stavros Tripakis. "Hybrid Co-simulation." In MODELS '18: ACM/IEEE 21th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3239372.3242896.

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5

HERZOG, Simon, Dennis ATABAY, Johannes JUNGWIRTH, and Vesna MIKULOVIC. "Self-adapting Building Models For Model Predictive Control." In 2017 Building Simulation Conference. IBPSA, 2013. http://dx.doi.org/10.26868/25222708.2013.2298.

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Turnitsa, Charles. "Heterogeneous models in a multi-model system." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822205.

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Sarma, Pallav, Wen H. Chen, and Jiang Xie. "Selecting Representative Models From a Large Set of Models." In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2013. http://dx.doi.org/10.2118/163671-ms.

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Chen, Qiwei, Tiexin Wang, Chengjie Lu, Tao Yue, and Shaukat Ali. "Enhancing the realism of autonomous driving simulation with real-time co-simulation." In MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3550356.3561558.

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David, Istvan, Jessie Galasso, and Eugene Syriani. "Inference of Simulation Models in Digital Twins by Reinforcement Learning." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00038.

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DOGAN, Timur, and Christoph REINHART. "Automated Conversion Of Architectural Massing Models Into Thermal ‘shoebox’ Models." In 2017 Building Simulation Conference. IBPSA, 2013. http://dx.doi.org/10.26868/25222708.2013.1123.

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Звіти організацій з теми "Models and simulation"

1

Ochs, Fabian, Mara Magni, Paolo Bonato, Matteo D’Antoni, David Geisler-Moroder, Martin Hauer, Samuel de Vries, and Roel Loonen. System Simulation Models. IEA SHC Task 56, April 2020. http://dx.doi.org/10.18777/ieashc-task56-2020-0004.

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2

Abdolmaleki, Kourosh. PR-453-205101-R02 On-Bottom Dynamic Simulation. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2023. http://dx.doi.org/10.55274/r0012257.

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Анотація:
This report presents a number of newly added features to the OBS software relevant to the dynamic simulation of pipelines. The additions include a function to extract on-bottom wave induced velocities and hydrodynamic forces on a given length of pipeline with arbitrary distribution of the nodes; a new dynamic simulation module to perform dynamic simulations for a single pipe node; two new soil models including the Verley and Sotberg for sand as well as a custom soil model as per the approach recommended in DNV-RP-F114. The added soil models are implemented in the dynamic solver, which provide full dynamic simulation of pipelines in two dimensions.
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3

Jensen, David. Learning Compositional Simulation Models. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada513896.

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4

Hemez, Francois M. Verifying and Validating Simulation Models. Office of Scientific and Technical Information (OSTI), February 2015. http://dx.doi.org/10.2172/1170703.

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5

Elvira, V. Daniel. Physics Models in Detector Simulation. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1570207.

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6

Field, Richard V. ,. Jr. Stochastic models: theory and simulation. Office of Scientific and Technical Information (OSTI), March 2008. http://dx.doi.org/10.2172/932886.

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7

Ekdahl, Jr., Carl. Cathode to Target Simulations for Scorpius: I. Simulation Codes and Models. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1764865.

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8

Ringhofer, Christian. Mathematical Models for VLSI Device Simulation. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada191125.

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9

Feng, Zhigang, Jianjun Miao, Adrian Peralta-Alva, and Manuel S. Santos. Numerical Simulation of Nonoptimal Dynamic Equilibrium Models. Federal Reserve Bank of St. Louis, 2009. http://dx.doi.org/10.20955/wp.2009.018.

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10

Scott Parker. Plasma Simulation Using Gyrokinetic-Gyrofluid Hybrid Models. Office of Scientific and Technical Information (OSTI), April 2009. http://dx.doi.org/10.2172/1010522.

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