Добірка наукової літератури з теми "Monte Carlo Simulation Technique"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Monte Carlo Simulation Technique".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Monte Carlo Simulation Technique"
JAKUMEIT, JÜRGEN. "COMPUTATIONAL ASPECTS OF THE LOCAL ITERATIVE MONTE CARLO TECHNIQUE." International Journal of Modern Physics C 11, no. 04 (June 2000): 665–73. http://dx.doi.org/10.1142/s0129183100000584.
Повний текст джерелаSuzuki, SHO, NAOKI Takano, and MITSUTERU ASAI. "F406 Monte Carlo Simulation of dynamic problem using Model Order Reduction Technique." Proceedings of The Computational Mechanics Conference 2011.24 (2011): _F—58_—_F—59_. http://dx.doi.org/10.1299/jsmecmd.2011.24._f-58_.
Повний текст джерелаSWENDSEN, ROBERT H., BRIAN DIGGS, JIAN-SHENG WANG, SHING-TE LI, CHRISTOPHER GENOVESE, and JOSEPH B. KADANE. "TRANSITION MATRIX MONTE CARLO." International Journal of Modern Physics C 10, no. 08 (December 1999): 1563–69. http://dx.doi.org/10.1142/s0129183199001340.
Повний текст джерелаMarjanovic, Srdjan, and Milovan Suvakov. "Monte Carlo simulation of positronium thermalization in gases." Chemical Industry 64, no. 3 (2010): 177–81. http://dx.doi.org/10.2298/hemind091221025m.
Повний текст джерелаNedjalkov, M., and P. Vitanov. "MONTE CARLO TECHNIQUE FOR SIMULATION OF HIGH ENERGY ELECTRONS." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 10, no. 4 (April 1991): 525–30. http://dx.doi.org/10.1108/eb051726.
Повний текст джерелаJuang, C. H., X. H. Huang, and D. J. Elton. "Fuzzy information processing by the Monte Carlo simulation technique." Civil Engineering Systems 8, no. 1 (March 1991): 19–25. http://dx.doi.org/10.1080/02630259108970602.
Повний текст джерелаFauzi, N. F. M., N. N. R. Roslan, and M. I. M. Ridzuan. "Low voltage reliability equivalent using monte-carlo simulation technique." IOP Conference Series: Materials Science and Engineering 863 (June 13, 2020): 012042. http://dx.doi.org/10.1088/1757-899x/863/1/012042.
Повний текст джерелаKosina, H., M. Nedjalkov, and S. Selberherr. "An event bias technique for Monte Carlo device simulation." Mathematics and Computers in Simulation 62, no. 3-6 (March 2003): 367–75. http://dx.doi.org/10.1016/s0378-4754(02)00245-8.
Повний текст джерелаLetosa, J., M. Garcia-Gracia, J. M. Fornies-Marquina, and J. M. Artacho. "Performance limits in TDR technique by Monte Carlo simulation." IEEE Transactions on Magnetics 32, no. 3 (May 1996): 958–61. http://dx.doi.org/10.1109/20.497401.
Повний текст джерелаSherniyozov, A. A., and Sh D. Payziyev. "Simulating optical processes: Monte Carlo photon tracing method." «Узбекский физический журнал» 24, no. 3 (September 11, 2022): 157–62. http://dx.doi.org/10.52304/.v24i3.357.
Повний текст джерелаДисертації з теми "Monte Carlo Simulation Technique"
Rangaraj, Dharanipathy. "Multicomponent aerosol dynamics : exploration of direct simulation Monte Carlo technique /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144452.
Повний текст джерелаJackson, Andrew N. "Structural phase behaviour via Monte Carlo techniques." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/4850.
Повний текст джерелаCan, Mutan Oya. "Comparison Of Regression Techniques Via Monte Carlo Simulation." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605175/index.pdf.
Повний текст джерелаLouvin, Henri. "Development of an adaptive variance reduction technique for Monte Carlo particle transport." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS351/document.
Повний текст джерелаThe Adaptive Multilevel Splitting algorithm (AMS) has recently been introduced to the field of applied mathematics as a variance reduction scheme for Monte Carlo Markov chains simulation. This Ph.D. work intends to implement this adaptative variance reduction method in the particle transport Monte Carlo code TRIPOLI-4, dedicated among others to radiation shielding and nuclear instrumentation studies. Those studies are characterized by strong radiation attenuation in matter, so that they fall within the scope of rare events analysis. In addition to its unprecedented implementation in the field of particle transport, two new features were developed for the AMS. The first is an on-the-fly scoring procedure, designed to optimize the estimation of multiple scores in a single AMS simulation. The second is an extension of the AMS to branching processes, which are common in radiation shielding simulations. For example, in coupled neutron-photon simulations, the neutrons have to be transported alongside the photons they produce. The efficiency and robustness of AMS in this new framework have been demonstrated in physically challenging configurations (particle flux attenuations larger than 10 orders of magnitude), which highlights the promising advantages of the AMS algorithm over existing variance reduction techniques
Nilsson, Emma. "Monte Carlo simulation techniques : The development of a general framework." Thesis, Linköping University, Department of Management and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18327.
Повний текст джерелаAlgorithmica Research AB develops software application for the financial markets. One of their products is Quantlab that is a tool for quantitative analyses. An effective method to value several financial instruments is Monte Carlo simulation. Since it is a common method Algorithmica is interesting in investigating if it is possible to create a Monte Carlo framework.
A requirement from Algorithmica is that the framework is general and this is the main problem to solve. It is difficult to generate a generalized framework because financial derivatives have very different appearances. To simplify the framework the thesis will be delimitated to European style derivatives where the underlying asset is following a Geometric Brownian Motion.
The definition of the problem and delimitation were defined gradually, in parallel with the review of literature, this to be able to decide what purpose, and delimitations that is reasonable to treat. Standard Monte Carlo requires a large number of trials and is therefore slow. To speed up the process there exist different variance reduction techniques and also Quasi Monte Carlo simulation, where deterministic numbers (low discrepancy sequences) is used instead of random. The thesis investigated the variance reduction techniques; control variate technique, antithetic variate technique, and the low discrepancy sequences; Sobol, Faure and Halton.
Three test instruments were chosen to test the framework, an Asian option and a Barrier option where the purpose is to conclude which Monte Carle method that performs best, and also a structured product; Smart Start, that is more complex and the purpose is to test that the framework can handle it.
To increase the understanding of the theory the Halton, Faure and Sobol sequence were implemented in Quantlab in parallel with the review of literature. The Halton and Faure sequences also seemed to perform worse than Sobol so they were not further analyzed.
The developing of the framework was an iterative process. The chosen solution is to design a general framework by using five function pointers; the path generator, the payoff function, the stop criterion function and the volatility and interest rates. The user specifies these functions by him/her given some obligatory input and output values. It is not a problem-free solution to use function pointers and several conflicts and issues are defined, therefore it is not recommended to implement the framework as it is designed today.
In parallel with the developing of the framework several experiments on the Asian and Barrier options were performed with varying result and it is not possible to draw a conclusion on which method that is best. Often Sobol seems to converge better and fluctuates less than standard Monte Carlo. The literature indicates that it is important that the user has an understanding of the instrument that should be valued, the stochastic process it follows and the advantages and disadvantages of different Monte Carlo methods. It is recommended to evaluate the different method with experiments, before deciding which method to use when valuing a new derivative.
Ahmad, Abdul Ossman. "Advances in an open-source direct simulation Monte Carlo technique for hypersonic rarefied gas flows." Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=26579.
Повний текст джерелаLester, Christopher. "Efficient simulation techniques for biochemical reaction networks." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:bb804e01-b1de-409f-b843-4806c2c990c2.
Повний текст джерелаBaker, Adam Richard Ernest. "The use of the Monte Carlo technique in the simulation of small-scale dosimeters and microdosimeters." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/2897/.
Повний текст джерелаMastail, Cédric. "Modélisation et simulation du dépôt des oxydes à forte permittivité par la technique du Monte-Carlo cinétique." Toulouse 3, 2009. http://thesesups.ups-tlse.fr/989/.
Повний текст джерелаMiniaturizing components requires radical changes in the development of future micro electronic devices. In this perspective, the gate dielectric of MOS devices can become so thin as to be made permeable to leakage currents. One solution is to replace SiO2 by a material with a higher permittivity which would allow the use of thicker layers with similar results. My work presents a multi-scale modelling of the growth of HfO2 on Si by atomic layer (ALD), which allows me to link the nano-structuration of an interface with the process of development. I demonstrate that knowing how basic chemical processes work, thanks to DFT calculations, allows considering a process simulation based on the development of a Kinetic Monte Carlo software named "HIKAD. " Going beyond rather obvious mechanisms, I introduce the notion of densification mechanisms of deposited oxide layers. These mechanisms are the key element to understand how the growth of the layer in terms of coverage works. But even beyond that aspect, they allow to study the system's evolution towards a massive material, starting from molecular reactions. I shall discuss all those points in the light of recent experimental characterisation results concerning the deposition of hafnium oxides
Mastail, Cedric. "Modélisation et simulation du dépôt des oxydes à forte permittivité par la technique du Monte-Carlo cinétique." Phd thesis, Université Paul Sabatier - Toulouse III, 2009. http://tel.archives-ouvertes.fr/tel-00541993.
Повний текст джерелаКниги з теми "Monte Carlo Simulation Technique"
Mun, Johnathan. Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. 2nd ed. New York: Wiley, 2010.
Знайти повний текст джерелаModeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. Hoboken, NJ: John Wiley & Sons, 2006.
Знайти повний текст джерелаMooney, Christopher. Monte Carlo Simulation. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1997. http://dx.doi.org/10.4135/9781412985116.
Повний текст джерелаMonte Carlo simulation. Thousand Oaks, Calif: Sage Publications, 1997.
Знайти повний текст джерелаZhu, Zhen, and Hari Rajagopalan. Monte Carlo Simulation. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908969.
Повний текст джерелаHess, Karl, ed. Monte Carlo Device Simulation. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7.
Повний текст джерелаThomopoulos, Nick T. Essentials of Monte Carlo Simulation. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6022-0.
Повний текст джерелаBrandimarte, Paolo. Handbook in Monte Carlo Simulation. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118593264.
Повний текст джерелаGleißner, Werner, and Marco Wolfrum. Risikoaggregation und Monte-Carlo-Simulation. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24274-9.
Повний текст джерелаPierre, L' Ecuyer, and Owen Art B, eds. Monte Carlo and quasi-Monte Carlo methods 2008. Heidelberg: Springer, 2009.
Знайти повний текст джерелаЧастини книг з теми "Monte Carlo Simulation Technique"
Frenkel, D. "Advanced Monte Carlo Techniques." In Computer Simulation in Chemical Physics, 93–152. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1679-4_4.
Повний текст джерелаWoolard, D. L., H. Tian, M. A. Littlejohn, R. J. Trew, and K. W. Kim. "The Application of Monte Carlo Techniques in Advanced Hydrodynamic Transport Models." In Monte Carlo Device Simulation, 219–66. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7_8.
Повний текст джерелаDemirtas, Hakan. "A Multiple Imputation Framework for Massive Multivariate Data of Different Variable Types: A Monte-Carlo Technique." In Monte-Carlo Simulation-Based Statistical Modeling, 143–62. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3307-0_8.
Повний текст джерелаSarrut, D., and N. Krah. "Artificial Intelligence and Monte Carlo Simulation." In Monte Carlo Techniques in Radiation Therapy, 251–58. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003211846-20.
Повний текст джерелаHanagaki, Kazunori, Junichi Tanaka, Makoto Tomoto, and Yuji Yamazaki. "Event Simulation." In Experimental Techniques in Modern High-Energy Physics, 115–23. Tokyo: Springer Japan, 2022. http://dx.doi.org/10.1007/978-4-431-56931-2_7.
Повний текст джерелаL'Ecuyer, Pierre, François Le Gland, Pascal Lezaud, and Bruno Tuffin. "Splitting Techniques." In Rare Event Simulation using Monte Carlo Methods, 39–61. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745403.ch3.
Повний текст джерелаThomson, Rowan M., Åsa Carlsson Tedgren, Gabriel Fonseca, Guillaume Landry, Brigitte Reniers, Mark J. Rivard, Jeffrey F. Williamson, and Frank Verhaegen. "Monte Carlo Simulation in Brachytherapy Patients and Applicator Modelling." In Monte Carlo Techniques in Radiation Therapy, 235–49. 2nd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003211846-19.
Повний текст джерелаShigeta, K., K. Tanaka, T. Iizuka, H. Kato, and H. Matsumoto. "A New Statistical Enhancement Technique in Parallelized Monte Carlo Device Simulation." In Simulation of Semiconductor Devices and Processes, 384–87. Vienna: Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-6619-2_93.
Повний текст джерелаSiwach, Vikash, Manju S. Tonk, and Hemant Poonia. "Simulation of Queues in Sugar Mills Using Monte Carlo Technique." In Springer Proceedings in Mathematics & Statistics, 481–93. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7272-0_33.
Повний текст джерелаAllen, Theodore T. "Variance Reduction Techniques and Quasi-Monte Carlo." In Introduction to Discrete Event Simulation and Agent-based Modeling, 111–24. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-139-4_8.
Повний текст джерелаТези доповідей конференцій з теми "Monte Carlo Simulation Technique"
Graham, G. E. "DZero Monte Carlo simulation." In ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH: VII International Workshop; ACAT 2000. AIP, 2001. http://dx.doi.org/10.1063/1.1405336.
Повний текст джерелаBaoxin Li, R. Chellappa, and Hankyu Moon. "Monte Carlo simulation techniques for probabilistic tracking." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.986883.
Повний текст джерелаAmini, Abolfazl M., George E. Ioup, and Juliette W. Ioup. "Metropolis Monte Carlo deconvolution technique compared to iterative methods for noisy data." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Ivan Kadar. SPIE, 2001. http://dx.doi.org/10.1117/12.436983.
Повний текст джерелаAbu Husain, M. K., N. I. Mohd Zaki, M. B. Johari, and G. Najafian. "Extreme Response Prediction for Fixed Offshore Structures by Monte Carlo Time Simulation Technique." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54200.
Повний текст джерелаDrosg, M., Marianne E. Hamm, and Robert W. Hamm. "Complete Monte Carlo Simulation of Neutron Scattering Experiments." In APPLICATIONS OF NUCLEAR TECHNIQUES: Eleventh International Conference. AIP, 2011. http://dx.doi.org/10.1063/1.3665300.
Повний текст джерелаBOWLES, ROLAND, TONY LAITURI, and GEORGE TREVINO. "A Monte Carlo simulation technique for low-altitude, wind-shear turbulence." In 28th Aerospace Sciences Meeting. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-564.
Повний текст джерелаSadowsky, J. S., and J. A. Bucklew. "Large deviations theory techniques in Monte Carlo simulation." In the 21st conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/76738.76804.
Повний текст джерелаPerry, Vincent, Wendy Gao, Michael Chen, J. Michael Barton, and Simon Su. "Visualization Techniques for Large-Scale Monte Carlo Simulation." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006423.
Повний текст джерелаIrimia, Daniel, and Jens O. M. Karlsson. "Monte Carlo Simulation of Ice Formation in Tissues." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32681.
Повний текст джерелаRen, Xiao-ping, Yun Yang, Fang Nan, Zi-yu Li, Jing-ru Chi, and Fan Yang. "An Indicator Determination Technique based on Delphi Approach and Monte Carlo Simulation." In 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8833173.
Повний текст джерелаЗвіти організацій з теми "Monte Carlo Simulation Technique"
Glaser, R. Monte Carlo simulation of scenario probability distributions. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/632934.
Повний текст джерелаXu, S. L., B. Lai, and P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/10134610.
Повний текст джерелаDouglas, L. J. Monte Carlo Simulation as a Research Management Tool. Office of Scientific and Technical Information (OSTI), June 1986. http://dx.doi.org/10.2172/1129252.
Повний текст джерелаAguayo Navarrete, Estanislao, Austin S. Ankney, Timothy J. Berguson, Richard T. Kouzes, John L. Orrell, Meredith D. Troy, and Clinton G. Wiseman. Monte Carlo Simulation Tool Installation and Operation Guide. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1095434.
Повний текст джерелаXu, S. L., B. Lai, and P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/5494991.
Повний текст джерелаBoyd, Lain D. Monte Carlo Simulation of Radiation in Hypersonic Flows. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada414031.
Повний текст джерелаClegg, Benjamin Wyatt, David H. Collins, Jr., and Aparna V. Huzurbazar. Petri Nets for Adversarial Models using Monte Carlo Simulation. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1473775.
Повний текст джерелаRensink, M. E., and T. D. Rognlien. Progress on coupling UEDGE and Monte-Carlo simulation codes. Office of Scientific and Technical Information (OSTI), August 1996. http://dx.doi.org/10.2172/380308.
Повний текст джерелаMartin, William R., James Paul Holloway, Kaushik Banerjee, Jesse Cheatham, and Jeremy Conlin. Global Monte Carlo Simulation with High Order Polynomial Expansions. Office of Scientific and Technical Information (OSTI), December 2007. http://dx.doi.org/10.2172/920974.
Повний текст джерелаHolliday, Mary R. Methodology of an Event-Driven Monte Carlo Missile Simulation. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada601300.
Повний текст джерела