Relevant bibliographies by topics / Monte Carlo simulation method

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

Academic literature on the topic 'Monte Carlo simulation method'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Monte Carlo simulation method"

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Takahashi, Akihiko, and Nakahiro Yoshida. "Monte Carlo Simulation with Asymptotic Method." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 35, no. 2 (2005): 171–203. http://dx.doi.org/10.14490/jjss.35.171.

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Alexander, Francis J., and Alejandro L. Garcia. "The Direct Simulation Monte Carlo Method." Computers in Physics 11, no. 6 (1997): 588. http://dx.doi.org/10.1063/1.168619.

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Rota, Gian-Carlo. "Simulation and the Monte-Carlo method." Advances in Mathematics 60, no. 1 (April 1986): 123. http://dx.doi.org/10.1016/0001-8708(86)90009-5.

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Date, Hiroyuki. "2. Monte Carlo Method and Simulation." Japanese Journal of Radiological Technology 70, no. 7 (2014): 705–14. http://dx.doi.org/10.6009/jjrt.2014_jsrt_70.7.705.

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Giles, Michael B. "Multilevel Monte Carlo methods." Acta Numerica 24 (April 27, 2015): 259–328. http://dx.doi.org/10.1017/s096249291500001x.

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Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs. Multilevel Monte Carlo is a recently developed approach which greatly reduces the computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and a high cost.In this article, we review the ideas behind the multilevel Monte Carlo method, and various recent generalizations and extensions, and discuss a number of applications which illustrate the flexibility and generality of the approach and the challenges in developing more efficient implementations with a faster rate of convergence of the multilevel correction variance.
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Mo, Wen Hui. "Monte Carlo Simulation of Reliability for Gear." Advanced Materials Research 268-270 (July 2011): 42–45. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.42.

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Production errors, material properties and applied loads of the gear are stochastic .Considering the influence of these stochastic factors, reliability of gear is studied. The sensitivity analysis of random variable can reduce the number of random variables. Simulating random variables, a lot of samples are generated. Using the Monte Carlo simulation based on the sensitivity analysis, reliabilities of contacting fatigue strength and bending fatigue strength can be obtained. The Monte Carlo simulation approaches the accurate solution gradually with the increase of the number of simulations. The numerical example validates the proposed method.
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Caflisch, Russel E. "Monte Carlo and quasi-Monte Carlo methods." Acta Numerica 7 (January 1998): 1–49. http://dx.doi.org/10.1017/s0962492900002804.

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Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasi-random (also called low-discrepancy) sequences, which are a deterministic alternative to random or pseudo-random sequences. The points in a quasi-random sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasi-Monte Carlo, has a convergence rate of approximately O((logN)kN−1). For quasi-Monte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less than the fluid dynamic length scale. Computational examples are presented throughout the text to illustrate the theory. A number of open problems are described.
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Rioux-Lavoie, Damien, Ryusuke Sugimoto, Tümay Özdemir, Naoharu H. Shimada, Christopher Batty, Derek Nowrouzezahrai, and Toshiya Hachisuka. "A Monte Carlo Method for Fluid Simulation." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–16. http://dx.doi.org/10.1145/3550454.3555450.

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We present a novel Monte Carlo-based fluid simulation approach capable of pointwise and stochastic estimation of fluid motion. Drawing on the Feynman-Kac representation of the vorticity transport equation, we propose a recursive Monte Carlo estimator of the Biot-Savart law and extend it with a stream function formulation that allows us to treat free-slip boundary conditions using a Walk-on-Spheres algorithm. Inspired by the Monte Carlo literature in rendering, we design and compare variance reduction schemes suited to a fluid simulation context for the first time, show its applicability to complex boundary settings, and detail a simple and practical implementation with temporal grid caching. We validate the correctness of our approach via quantitative and qualitative evaluations - across a range of settings and domain geometries - and thoroughly explore its parameters' design space. Finally, we provide an in-depth discussion of several axes of future work building on this new numerical simulation modality.
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Gelman, Andrew. "Method of Moments Using Monte Carlo Simulation." Journal of Computational and Graphical Statistics 4, no. 1 (March 1995): 36. http://dx.doi.org/10.2307/1390626.

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Gelman, Andrew. "Method of Moments Using Monte Carlo Simulation." Journal of Computational and Graphical Statistics 4, no. 1 (March 1995): 36–54. http://dx.doi.org/10.1080/10618600.1995.10474664.

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Dissertations / Theses on the topic "Monte Carlo simulation method"

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Janzon, Krister. "Monte Carlo Path Simulation and the Multilevel Monte Carlo Method." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-151975.

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A standard problem in the field of computational finance is that of pricing derivative securities. This is often accomplished by estimating an expected value of a functional of a stochastic process, defined by a stochastic differential equation (SDE). In such a setting the random sampling algorithm Monte Carlo (MC) is useful, where paths of the process are sampled. However, MC in its standard form (SMC) is inherently slow. Additionally, if the analytical solution to the underlying SDE is not available, a numerical approximation of the process is necessary, adding another layer of computational complexity to the SMC algorithm. Thus, the computational cost of achieving a certain level of accuracy of the estimation using SMC may be relatively high. In this thesis we introduce and review the theory of the SMC method, with and without the need of numerical approximation for path simulation. Two numerical methods for path approximation are introduced: the Euler–Maruyama method and Milstein's method. Moreover, we also introduce and review the theory of a relatively new (2008) MC method – the multilevel Monte Carlo (MLMC) method – which is only applicable when paths are approximated. This method boldly claims that it can – under certain conditions – eradicate the additional complexity stemming from the approximation of paths. With this in mind, we wish to see whether this claim holds when pricing a European call option, where the underlying stock process is modelled by geometric Brownian motion. We also want to compare the performance of MLMC in this scenario to that of SMC, with and without path approximation. Two numerical experiments are performed. The first to determine the optimal implementation of MLMC, a static or adaptive approach. The second to illustrate the difference in performance of adaptive MLMC and SMC – depending on the used numerical method and whether the analytical solution is available. The results show that SMC is inferior to adaptive MLMC if numerical approximation of paths is needed, and that adaptive MLMC seems to meet the complexity of SMC with an analytical solution. However, while the complexity of adaptive MLMC is impressive, it cannot quite compensate for the additional cost of approximating paths, ending up roughly ten times slower than SMC with an analytical solution.
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Lee, Ming Ripman, and 李明. "Monte Carlo simulation for confined electrolytes." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31240513.

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Lee, Ming Ripman. "Monte Carlo simulation for confined electrolytes /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22055009.

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Stephen, Alexander. "Enhancement of thermionic cooling using Monte Carlo simulation." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=210113.

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Advances in the field of semiconductor physics have allowed for rapid development of new, more powerful devices. The new fabrication techniques allow for reductions in device geometry, increasing the possible wafer packing density. The increased output power comes with the price of excessive heat generation, the removal of which proves problematic at such scales for conventional cooling systems. Consequently, there is a rising demand for new cooling systems, preferably those that do not add large amount of additional bulk to the system. One promising system is the thermoelectric (TE) cooler which is small enough to be integrated onto the device wafer. Unlike more traditional gas and liquid coolers, TE coolers do not require moving parts or external liquid reservoirs, relying only on the flow of electrons to transport heat energy away from the device. Although TE cooling provides a neat solution for the extraction of heat from micron scale devices, it can normally only produce small amounts of cooling of 1-2 Kelvin, limiting its application to low power devices. This research aimed to find ways to enhance the performance of the TE cooler using detailed simulation analysis. For this, a self consistent, semi-classical, ensemble Monte Carlo model was designed to investigate the operation of the TE cooler at a higher level than would be possible with experimental measurements alone. As part of its development, the model was validated on a variety of devices including a Gunn diode and two micro-cooler designs from the literature, one which had been previously simulated and another which had been experimentally analysed. When applied to the TE cooler of focus, novel operational data was obtained and signification improvements in cooling power were found with only minor alterations to the device structure and without need for an increase in volume.
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Armour, Jessica D. "On the Gap-Tooth direct simulation Monte Carlo method." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72863.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, February 2012.
"February 2012." Cataloged from PDF version of thesis.
Includes bibliographical references (p. [73]-74).
This thesis develops and evaluates Gap-tooth DSMC (GT-DSMC), a direct Monte Carlo simulation procedure for dilute gases combined with the Gap-tooth method of Gear, Li, and Kevrekidis. The latter was proposed as a means of reducing the computational cost of microscopic (e.g. molecular) simulation methods using simulation particles only in small regions of space (teeth) surrounded by (ideally) large gaps. This scheme requires an algorithm for transporting particles between teeth. Such an algorithm can be readily developed and implemented within direct Monte Carlo simulations of dilute gases due to the non-interacting nature of the particle-simulators. The present work develops and evaluates particle treatment at the boundaries associated with diffuse-wall boundary conditions and investigates the drawbacks associated with GT-DSMC implementations which detract from the theoretically large computational benefit associated with this algorithm (the cost reduction is linear in the gap-to-tooth ratio). Particular attention is paid to the additional numerical error introduced by the gap-tooth algorithm as well as the additional statistical uncertainty introduced by the smaller number of particles. We find the numerical error introduced by transporting particles to adjacent teeth to be considerable. Moreover, we find that due to the reduced number of particles in the simulation domain, correlations persist longer, and thus statistical uncertainties are larger than DSMC for the same number of particles per cell. This considerably reduces the computational benefit associated with the GT-DSMC algorithm. We conclude that the GT-DSMC method requires more development, particularly in the area of error and uncertainty reduction, before it can be used as an effective simulation method.
by Jessica D. Armour.
S.M.
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Obradovic, Borna Josip. "Multi-dimensional Monte Carlo simulation of ion implantation into complex structures /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Blanckenberg, J. P. (Jacobus Petrus). "Monte Carlo simulation of direction sensitive antineutrino detection." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/2885.

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Thesis (MSc (Physics))--University of Stellenbosch, 2010.
ENGLISH ABSTRACT: Neutrino and antineutrino detection is a fairly new eld of experimental physics, mostly due to the small interaction cross section of these particles. Most of the detectors in use today are huge detectors consisting of kilotons of scintilator material and large arrays of photomultiplier tubes. Direction sensitive antineutrino detection has however, not been done (at the time of writing of this thesis). In order to establish the feasibility of direction sensitive antineutrino detection, a Monte Carlo code, DSANDS, was written to simulate the detection process. This code focuses on the neutron and positron (the reaction products after capture on a proton) transport through scintilator media. The results are then used to determine the original direction of the antineutrino, in the same way that data from real detectors would be used, and to compare it with the known direction. Further investigation is also carried out into the required amount of statistics for accurate results in an experimental eld where detection events are rare. Results show very good directional sensitivity of the detection method.
AFRIKAANSE OPSOMMING: Neutrino en antineutrino meting is 'n relatief nuwe veld in eksperimentele sika, hoofsaaklik as gevolg van die klein interaksie deursnee van hierdie deeltjies. Die meeste hedendaagse detektors is massiewe detektors met kilotonne sintilator materiaal en groot aantalle fotovermenigvuldiger buise. Tans is rigting sensitiewe antineutrino metings egter nog nie uit gevoer nie. 'n Monte Carlo kode, DSANDS, is geskryf om die meet proses te simuleer en sodoende die uitvoerbaarheid van rigting sensitiewe antineutrino metings vas te stel. Hierdie kode fokus op die beweging van neutrone en positrone (die reaksie produkte) deur die sintilator medium. Die resultate word dan gebruik om die oorspronklike rigting van die antineutrino te bepaal, soos met data van regte detektors gedoen sou word, en te vergelyk met die bekende oorspronklike rigting van die antineutrino. Verder word daar ook gekyk na die hoeveelheid statistiek wat nodig sal wees om akkurate resultate te kry in 'n veld waar metings baie skaars is. Die resultate wys baie goeie rigting sensitiwiteit van die meet metode.
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Mansour, Nabil S. "Inclusion of electron-plasmon interactions in ensemble Monte Carlo simulations of degerate GaAs." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/13862.

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Rumbe, George Otieno. "Performance evaluation of second price auction using Monte Carlo simulation." Diss., Online access via UMI:, 2007.

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Junnarkar, Parikshit Manoj. "Monte-Carlo simulation of photoproduction of Omega meson." Master's thesis, Mississippi State : Mississippi State University, 2006. http://library.msstate.edu/etd/show.asp?etd=etd-07312006-013358.

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Books on the topic "Monte Carlo simulation method"

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Monte Carlo simulation. Thousand Oaks, Calif: Sage Publications, 1997.

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P, Kroese Dirk, ed. Simulation and the monte carlo method. 2nd ed. Hoboken, N.J: John Wiley & Sons, 2008.

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Kroese, Dirk P., Thomas Taimre, Zdravko I. Botev, and Rueven Y. Rubinstein. Simulation and the Monte Carlo Method. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2007. http://dx.doi.org/10.1002/9780470285312.

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Rubinstein, Reuven Y., and Dirk P. Kroese. Simulation and the Monte Carlo Method. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118631980.

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Moglestue, C. Monte Carlo simulation of semiconductor devices. London: Chapman & Hall, 1993.

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Pierre, L' Ecuyer, and Owen Art B, eds. Monte Carlo and quasi-Monte Carlo methods 2008. Heidelberg: Springer, 2009.

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Monte Carlo optimization, simulation, and sensitivity of queueing networks. New York: Wiley, 1986.

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Franklin, Mendivil, ed. Explorations in Monte Carlo methods. Dordrecht: Springer, 2009.

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Moglestue, C. Monte Carlo Simulation of Semiconductor Devices. Dordrecht: Springer Netherlands, 1993.

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Monte Carlo simulation with applications to finance. Boca Raton: CRC Press, 2012.

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Book chapters on the topic "Monte Carlo simulation method"

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Jungemann, Christoph, and Bernd Meinerzhagen. "The Monte-Carlo Method." In Hierarchical Device Simulation, 34–56. Vienna: Springer Vienna, 2003. http://dx.doi.org/10.1007/978-3-7091-6086-2_3.

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Cevallos-Torres, Lorenzo, and Miguel Botto-Tobar. "Monte Carlo Simulation Method." In Problem-Based Learning: A Didactic Strategy in the Teaching of System Simulation, 87–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13393-1_5.

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Tildesley, D. J. "The Monte Carlo Method." In Computer Simulation in Chemical Physics, 1–22. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1679-4_1.

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Kinser, Jason M. "The Monte Carlo Method." In Modeling and Simulation in Python, 27–54. New York: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003226581-4.

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Rollett, Anthony D., and Priya Manohar. "The Monte Carlo Method." In Continuum Scale Simulation of Engineering Materials, 77–114. Weinheim, FRG: Wiley-VCH Verlag GmbH & Co. KGaA, 2005. http://dx.doi.org/10.1002/3527603786.ch4.

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Moglestue, C. "The Monte Carlo Method." In Monte Carlo Simulation of Semiconductor Devices, 115–29. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8133-2_5.

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Zio, Enrico. "Monte Carlo Simulation: The Method." In Springer Series in Reliability Engineering, 19–58. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4588-2_3.

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Hu, Xiao, Yoshihiko Nonomura, and Masanori Kohno. "Monte Carlo Simulation." In Springer Handbook of Materials Measurement Methods, 1057–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-30300-8_22.

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Chang, Mark. "Monte Carlo Simulation." In Modern Issues and Methods in Biostatistics, 233–59. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9842-2_9.

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McNeish, Daniel, Stephanie Lane, and Patrick Curran. "Monte Carlo Simulation Methods." In The Reviewer’s Guide to Quantitative Methods in the Social Sciences, 269–76. Second Edition. | New York : Routledge, 2019. | Revised edition of The reviewer’s guide to quantitative methods in the social sciences, 2010.: Routledge, 2018. http://dx.doi.org/10.4324/9781315755649-20.

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Conference papers on the topic "Monte Carlo simulation method"

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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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Harrison, Robert L., Carlos Granja, and Claude Leroy. "Introduction to Monte Carlo Simulation." In NUCLEAR PHYSICS METHODS AND ACCELERATORS IN BIOLOGY AND MEDICINE: Fifth International Summer School on Nuclear Physics Methods and Accelerators in Biology and Medicine. AIP, 2010. http://dx.doi.org/10.1063/1.3295638.

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Tan, Hui. "Adaptive Monte Carlo sampling gradient method for optimization." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248222.

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Vedula, Prakash, and Dustin Otten. "Importance Sampling Based Direct Simulation Monte Carlo Method." In 10th AIAA/ASME Joint Thermophysics and Heat Transfer Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-5061.

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Saragih, Nidia Enjelita, Ermayanti Astuti, Austin Alexander Parhusip, and Tika Nirmalasari. "Determining Production Number Using Monte Carlo Simulation Method." In 2018 6th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2018. http://dx.doi.org/10.1109/citsm.2018.8674304.

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Dai, Jian-Yang, Zai-Fa Zhou, Qing-An Huang, and Wei-Hua Li. "LPCVD process simulation based on Monte Carlo method." In 2010 10th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT). IEEE, 2010. http://dx.doi.org/10.1109/icsict.2010.5667771.

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Kelsall, R. W. "The Monte Carlo method for semiconductor device simulation." In IEE Colloquium on Physical Modelling of Semiconductor Devices. IEE, 1995. http://dx.doi.org/10.1049/ic:19950428.

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Kalos, Malvin H. "Monte Carlo methods in the physical sciences." In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419611.

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Schrock, Christopher, and Aihua Wood. "Distributional Direct Simulation Monte Carlo Methods." In 10th AIAA/ASME Joint Thermophysics and Heat Transfer Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-4501.

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Goswami, Somdatta, and Subrata Chakraborty. "Adaptive Response Surface Method Based Efficient Monte Carlo Simulation." In Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA). Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413609.205.

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Reports on the topic "Monte Carlo simulation method"

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Boyd, Iain D. A Threshold Line Dissociation Model for the Direct Simulation Monte Carlo Method,. Fort Belvoir, VA: Defense Technical Information Center, May 1996. http://dx.doi.org/10.21236/ada324950.

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Garcia, A. L., F. Baras, and M. M. Mansour. Comment on ``Simulation of a two-dimensional Rayleigh-Benard system using the direct simulation Monte Carlo method``. Office of Scientific and Technical Information (OSTI), June 1994. http://dx.doi.org/10.2172/371414.

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Glaser, R., G. Johannesson, S. Sengupta, B. Kosovic, S. Carle, G. Franz, R. Aines, et al. Stochastic Engine Final Report: Applying Markov Chain Monte Carlo Methods with Importance Sampling to Large-Scale Data-Driven Simulation. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/15009813.

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J. Case and D. Buesch. Simulation of Ventilation Efficiency, Temperatures, and Relative Humidities in Emplacement Drifts at Yucca Mountain, Nevada, Using Monte Carlo and Composite Thermal-Pulse Methods. Office of Scientific and Technical Information (OSTI), April 2004. http://dx.doi.org/10.2172/837500.

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Hill, James Lloyd. Introduction to the Monte Carlo Method. Office of Scientific and Technical Information (OSTI), June 2020. http://dx.doi.org/10.2172/1634920.

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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.

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Blomquist, R. N., and E. M. Gelbard. Alternative implementations of the Monte Carlo power method. Office of Scientific and Technical Information (OSTI), March 2002. http://dx.doi.org/10.2172/793906.

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Xu, 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.

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Douglas, 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.

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Aguayo 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.

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