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Relevant bibliographies by topics / Simulation methods

Academic literature on the topic 'Simulation methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Simulation methods"

1

Ripley, B. D., and M. D. Kirkland. "Iterative simulation methods." Journal of Computational and Applied Mathematics 31, no. 1 (July 1990): 165–72. http://dx.doi.org/10.1016/0377-0427(90)90347-3.

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Fagbade, Adeyemi, and Stefan Heinz. "Continuous Eddy Simulation vs. Resolution-Imposing Simulation Methods for Turbulent Flows." Fluids 9, no. 1 (January 10, 2024): 22. http://dx.doi.org/10.3390/fluids9010022.

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The usual concept of simulation methods for turbulent flows is to impose a certain (partial) flow resolution. This concept becomes problematic away from limit regimes of no or an almost complete flow resolution: discrepancies between the imposed and actual flow resolution may imply an unreliable model behavior and high computational cost to compensate for simulation deficiencies. An exact mathematical approach based on variational analysis provides a solution to these problems. Minimal error continuous eddy simulation (CES) designed in this way enables simulations in which the model actively responds to variations in flow resolution by increasing or decreasing its contribution to the simulation as required. This paper presents the first application of CES methods to a moderately complex, relatively high Reynolds number turbulent flow simulation: the NASA wall-mounted hump flow. It is shown that CES performs equally well or better than almost resolving simulation methods at a little fraction of computational cost. Significant computational cost and performance advantages are reported in comparison to popular partially resolving simulation methods including detached eddy simulation and wall-modeled large eddy simulation. Characteristic features of the asymptotic flow structure are identified on the basis of CES simulations.

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Podolskaya, Nina A. "Network Simulation: Tasks and Methods of Their Solution." International Journal of Computer Theory and Engineering 6, no. 5 (October 2014): 392–95. http://dx.doi.org/10.7763/ijcte.2014.v6.896.

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Zäh, Michael F., and Alexander Schober. "Innovative welding simulation methods." ATZproduktion worldwide 3, no. 1 (February 2010): 32–36. http://dx.doi.org/10.1007/bf03224215.

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Millington, James D. A., and John Wainwright. "Mixed qualitative-simulation methods." Progress in Human Geography 41, no. 1 (July 10, 2016): 68–88. http://dx.doi.org/10.1177/0309132515627021.

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Across geography there has been variable engagement with the use of simulation and agent-based modelling. We argue that agent-based simulation provides a complementary method to investigate geographical issues which need not be used in ways that are epistemologically different in kind from some other approaches in contemporary geography. We propose mixed qualitative-simulation methods that iterate back-and-forth between ‘thick’ (qualitative) and ‘thin’ (simulation) approaches and between the theory and data they produce. These mixed methods accept simulation modelling as process and practice; a way of using computers with concepts and data to ensure social theory remains embedded in day-to-day geographical thinking.

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Andersen, Torben G. "SIMULATION-BASED ECONOMETRIC METHODS." Econometric Theory 16, no. 1 (February 2000): 131–38. http://dx.doi.org/10.1017/s0266466600001080.

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The accessibility of high-performance computing power has always influenced theoretical and applied econometrics. Gouriéroux and Monfort begin their recent offering, Simulation-Based Econometric Methods, with a stylized three-stage classification of the history of statistical econometrics. In the first stage, lasting through the 1960's, models and estimation methods were designed to produce closed-form expressions for the estimators. This spurred thorough investigation of the standard linear model, linear simultaneous equations with the associated instrumental variable techniques, and maximum likelihood estimation within the exponential family. During the 1970's and 1980's the development of powerful numerical optimization routines led to the exploration of procedures without closed-form solutions for the estimators. During this period the general theory of nonlinear statistical inference was developed, and nonlinear micro models such as limited dependent variable models and nonlinear time series models, e.g., ARCH, were explored. The associated estimation principles included maximum likelihood (beyond the exponential family), pseudo-maximum likelihood, nonlinear least squares, and generalized method of moments. Finally, the third stage considers problems without a tractable analytic criterion function. Such problems almost invariably arise from the need to evaluate high-dimensional integrals. The idea is to circumvent the associated numerical problems by a simulation-based approach. The main requirement is therefore that the model may be simulated given the parameters and the exogenous variables. The approach delivers simulated counterparts to standard estimation procedures and has inspired the development of entirely new procedures based on the principle of indirect inference.

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Tikhonov, V., and R. Veenhof. "GEM simulation methods development." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 478, no. 1-2 (February 2002): 452–59. http://dx.doi.org/10.1016/s0168-9002(01)01801-0.

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Guha, Ratan, and Mostafa Bassiouni. "Simulation Methods and Applications." Simulation Practice and Theory 9, no. 3-5 (April 2002): 91–93. http://dx.doi.org/10.1016/s0928-4869(01)00056-8.

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Elber, Ron. "Long-timescale simulation methods." Current Opinion in Structural Biology 15, no. 2 (April 2005): 151–56. http://dx.doi.org/10.1016/j.sbi.2005.02.004.

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Barrett, John H. "Methods of channeling simulation." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 44, no. 3 (January 1990): 367–72. http://dx.doi.org/10.1016/0168-583x(90)90652-b.

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Dissertations / Theses on the topic "Simulation methods"

1

Azhar, Mueed [Verfasser], and Jan G. [Akademischer Betreuer] Korvink. "Simulation of NMR experiments using particle simulation methods." Freiburg : Universität, 2018. http://d-nb.info/1155722485/34.

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Mauron, Laurent. "Pedestrians simulation methods Diploma thesis /." Zürich : ETH, Eidgenössische Technische Hochschule Zürich, [Department of Computer Science, Simulation Group], 2002. http://e-collection.ethbib.ethz.ch/show?type=dipl&nr=136.

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Bekker, Hendrik. "Molecular dynamics simulation methods revised." [Groningen] : [Groningen] : Rijksuniversiteit Groningen ; [University Library Groningen] [Host], 1996. http://irs.ub.rug.nl/ppn/14860532X.

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Low, Hamish Wallace. "Simulation methods and economic analysis." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392495.

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Vidal-Codina, Ferran. "Simulation methods for plasmonic structures." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112460.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 129-148).
In the recent years there has been a growing interest in studying electromagnetic wave propagation at the nanoscale. The interaction of light with metallic nanostructures produces a collective excitation of conduction electrons at the metal surface, also known as surface plasmons. These plasmonic resonances enable an unprecedented control of light by confining the electromagnetic field to regions well beyond the diffraction limit, thereby leading to nearfield enhancements of the incident wave of several orders of magnitude. These remarkable properties have motivated the application of plasmonic devices in sensing, nano-resolution imaging, energy harvesting, nanoscale electronics and cancer treatment. Despite state-of-the-art nanofabrication techniques are used to realize plasmonic devices, their performance is severely impacted by fabrication uncertainties arising from extreme manufacturing constraints. Mathematical modeling and numerical simulation are therefore essential to accurately predict the response of the physical system, and must be incorporated in the design process. Nonetheless, plasmonic simulations present notable challenges. From the physical perspective, the realistic behavior of conduction electrons in metallic nanostructures is not captured by Maxwell's equations, thus requiring additional modeling. From the simulation perspective, the disparity in length scales stemming from the extreme field localization exceeds the capabilities of most numerical simulation schemes. In addition, relevant data such as optical constants or geometry specifications are typically subject to measurement and manufacturing errors, hence simulations need to accommodate uncertainty in the data. In this thesis we present a collection of numerical methods to efficiently simulate electromagnetic wave propagation through metallic nanostructures. Firstly, we develop the hybridizable discontinuous Galerkin (HDG) method for Maxwell's equations augmented with the hydrodynamic model for metals, which accounts for the nonlocal interactions between electrons that become predominant at nanometric regimes. Secondly, we develop a reduced order modeling (ROM) framework for Maxwell's equations with the HDG method, enabling the incorporation of material and geometric uncertainties in the simulations. The result is a family of surrogate models that produces accurate yet inexpensive simulations of plasmonic devices. Finally, we apply these approaches to the study of periodic annular nanogaps, and present parametric analyses, verification with experimental data and design of novel structures.
by Ferran Vidal-Codina.
Ph. D.

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Fithen, Robert Miller. "Adaptive finite element simulation of incompressible viscous flow." Diss., This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-06062008-170423/.

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Isaksson, Erik. "Simulation methods for bumper system development." Licentiate thesis, Luleå : Luleå tekniska universitet/Tillämpad fysik, maskin- och materialteknik/Hållfasthetslära, 2006. http://epubl.ltu.se/1402-1757/2006/55/LTU-LIC-0655-SE.pdf.

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Homem, de Mello Tito. "Simulation-based methods for stochastic optimization." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/24846.

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Denison, David George Taylor. "Simulation based Bayesian nonparametric regression methods." Thesis, Imperial College London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266105.

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Sturdy, Yvette Katherine. "Molecular simulation with path integral methods." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436950.

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Books on the topic "Simulation methods"

1

Michael, Kotelyanskii, and Theodorou Doros Nicolas, eds. Simulation methods for polymers. New York: Marcel Dekker, 2004.

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2

1943-, Monfort Alain, ed. Simulation-based econometric methods. Oxford: Oxford University Press, 1996.

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3

Haile, J. M. Molecular dynamics simulation: Elementary methods. New York: Wiley, 1992.

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Balakrishnan, N., V. B. Melas, and S. Ermakov, eds. Advances in Stochastic Simulation Methods. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-1318-5.

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Joppich, Wolfgang, and Slobodan Mijalković. Multigrid Methods for Process Simulation. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-9253-5.

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Beer, Gernot, and Stéphane Bordas, eds. Isogeometric Methods for Numerical Simulation. Vienna: Springer Vienna, 2015. http://dx.doi.org/10.1007/978-3-7091-1843-6.

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Ross, Richard B., and Sanat Mohanty, eds. Multiscale Simulation Methods for Nanomaterials. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470191675.

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8

M, Cerrolaza, Gajardo C, and Brebbia C. A, eds. Numerical methods in engineering simulation. Southampton: Computational Mechanics Publication, 1996.

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9

Joppich, Wolfgang. Multigrid Methods for Process Simulation. Vienna: Springer Vienna, 1993.

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Balakrishnan, N., S. M. Ermakov, and V. B. Melas. Advances in stochastic simulation methods. New York: Springer, 2000.

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Book chapters on the topic "Simulation methods"

1

Deutsch, Hans-Peter, and Mark W. Beinker. "Simulation Methods." In Derivatives and Internal Models, 559–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22899-6_23.

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2

Ivory, Melody Y. "Simulation Methods." In Automated Web Site Evaluation, 53–56. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0375-8_7.

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3

Vorländer, Michael. "Simulation Methods." In Auralization, 145–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51202-6_10.

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4

Wasserman, Larry. "Simulation Methods." In Springer Texts in Statistics, 403–33. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-21736-9_24.

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Deutsch, Hans-Peter. "Simulation Methods." In Derivatives and Internal Models, 427–33. London: Palgrave Macmillan UK, 2004. http://dx.doi.org/10.1057/9781403946089_23.

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6

Hellström, Sten. "Simulation methods." In ESD — The Scourge of Electronics, 128–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-80302-4_10.

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Müller, Mark, and Dietmar Pfahl. "Simulation Methods." In Guide to Advanced Empirical Software Engineering, 117–52. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-044-5_5.

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Albrecher, Hansjoerg, Andreas Binder, Volkmar Lautscham, and Philipp Mayer. "Simulation Methods." In Compact Textbooks in Mathematics, 117–31. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0519-3_11.

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Engel, Megan Clare. "Simulation Methods." In DNA Systems Under Internal and External Forcing, 19–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25413-1_2.

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Deutsch, Hans-Peter. "Simulation Methods." In Derivatives and Internal Models, 419–25. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1057/9780230502109_23.

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Conference papers on the topic "Simulation methods"

1

Kippe, Vegard, Haakon Haegland, and Knut-Andreas Lie. "A Method To Improve the Mass Balance in Streamline Methods." In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/106250-ms.

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Bertiger, W. I., and F. J. Kelsey. "Inexact Adaptive Newton Methods." In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 1985. http://dx.doi.org/10.2118/13501-ms.

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Riether, Gernot, and Tom Butler. "Simulation Space." In eCAADe 2008: Architecture "in computro" - Integrating methods and techniques. eCAADe, 2008. http://dx.doi.org/10.52842/conf.ecaade.2008.133.

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Riether, Gernot, and Tom Butler. "Simulation Space." In eCAADe 2008: Architecture "in computro" - Integrating methods and techniques. eCAADe, 2008. http://dx.doi.org/10.52842/conf.ecaade.2008.133.

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Florez, Horacio, Mary Wheeler, and Adolfo Rodriguez. "Domain Decomposition Methods in Geomechanics." In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2013. http://dx.doi.org/10.2118/163674-ms.

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Basden, Alastair, Richard Myers, and Timothy Butterley. "Monte-Carlo simulation of EAGLE." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/aopt.2009.aotud4.

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M. J. Tarokh. "Supply Chain Simulation Methods." In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2006. http://dx.doi.org/10.1109/soli.2006.236571.

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Tarokh, M. J., and M. Golkar. "Supply Chain Simulation Methods." In 2006 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2006. http://dx.doi.org/10.1109/soli.2006.329066.

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Wilsey, Phil. "Session details: Simulation methods." In SIGSIM-PADS '14: SIGSIM Principles of Advanced Discrete Simulation. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/3247545.

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Bünning, Felix, Corentin Pfister, Ahmed Aboudonia, Philipp Heer, and John Lygeros. "Comparing Machine Learning based Methods to standard Regression Methods for MPC on a virtual Testbed." In 2021 Building Simulation Conference. KU Leuven, 2021. http://dx.doi.org/10.26868/25222708.2021.30346.

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Reports on the topic "Simulation methods"

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Booth, T. E., J. A. Carlson, and R. A. Forster. Simulation methods for advanced scientific computing. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/674863.

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Frazier, Peter I. Decision-Theoretic Methods in Simulation Optimization. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada610908.

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Glynn, Peter W., and Donald L. Iglehart. Simulation Methods for Queues: An Overview. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada197084.

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Carroll, William L. Daylighting simulation: methods, algorithms, and resources. Office of Scientific and Technical Information (OSTI), December 1999. http://dx.doi.org/10.2172/861173.

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Xiao, Shengyou. Multigrid methods with applications to reservoir simulation. Office of Scientific and Technical Information (OSTI), May 1994. http://dx.doi.org/10.2172/418388.

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Glowinsky, Roland, Anthony J. Kearsley, Tsorng-Whay Pan, and Jacques Periaux. Fictitious Domain Methods for Viscous Flow Simulation. Fort Belvoir, VA: Defense Technical Information Center, May 1995. http://dx.doi.org/10.21236/ada445628.

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Pellegrini, Claudio. Theoretical and simulation studies of seeding methods. Office of Scientific and Technical Information (OSTI), December 2017. http://dx.doi.org/10.2172/1412634.

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Cai, Yunhai. Methods and Issues in Beam-Beam Simulation. Office of Scientific and Technical Information (OSTI), October 2001. http://dx.doi.org/10.2172/798894.

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Greengard, Leslie. Novel Methods for Electromagnetic Simulation and Design. Fort Belvoir, VA: Defense Technical Information Center, August 2016. http://dx.doi.org/10.21236/ad1012909.

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Friedman, A., and E. Sonnendrucker. Some Aspects of Non-Split Vlasov Simulation Methods. Office of Scientific and Technical Information (OSTI), August 2002. http://dx.doi.org/10.2172/15002126.

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