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Artykuły w czasopismach na temat „Monte Carlo Simulation”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 9 czerwca 2025

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1

Davidović, Branko, Duško Letić, and Aleksandar Jovanović. "MONTE CARLO SIMULATION IN INTRALOGISTICS." MEST Journal 2, no. 1 (January 15, 2014): 87–93. http://dx.doi.org/10.12709/mest.02.02.01.09.

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2

Ziegel, Eric R., and C. Mooney. "Monte Carlo Simulation." Technometrics 40, no. 3 (August 1998): 267. http://dx.doi.org/10.2307/1271205.

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3

Chen, Jiming. "Monte Carlo Simulations in Complex Systems: Challenges and New Approaches." Theoretical and Natural Science 86, no. 1 (January 15, 2025): 114–19. https://doi.org/10.54254/2753-8818/2025.20172.

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Monte Carlo simulations are crucial for examining the Ising model, especially when it's tough to find analytical solutions. However, traditional Monte Carlo methods, like the Metropolis algorithm, encounter significant hurdles, such as slowing down near phase transitions and issues related to finite sizes. This paper looks into both the advantages and limitations of these traditional Monte Carlo techniques. It also covers recent developments like Tensor Network Monte Carlo and Quantum Monte Carlo methods, which have shown promise in overcoming these challenges. Furthermore, the paper explores how machine learning techniques are being incorporated into Monte Carlo simulations to enhance their efficiency and accuracy. These advancements represent a major leap forward in simulating complex systems and have expanded the use of Monte Carlo methods into new areas, including quantum systems and models with disorder. This paper finds that traditional Monte Carlo methods, like the Metropolis algorithm, face issues such as critical slowing down and finite-size effects. New methods, such as Tensor Network Monte Carlo (TNMC) and Quantum Monte Carlo (QMC), effectively address these challenges, enhancing simulation accuracy and efficiency in complex systems. Additionally, integrating machine learning optimizes parameters and accelerates convergence, expanding Monte Carlos applicability to large-scale, high-dimensional systems across various scientific fields.

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4

Sakota, Daisuke, and Setsuo Takatani. "Photon-cell interactive Monte Carlo simulation." Nippon Laser Igakkaishi 32, no. 4 (2012): 411–20. http://dx.doi.org/10.2530/jslsm.32.411.

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5

Dou, Mingze. "Principle and Applications of Monte-Carlo Simulation in Forecasting, Algorithm and Health Risk Assessment." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 406–14. http://dx.doi.org/10.54097/jjw5by20.

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Monte Carlo simulation, as a technique to reverse parameters by random sampling in known data, is widely used in many fields such as finance, computer and engineering. While introducing the basic concepts and related principles of Monte Carlo simulation, this paper will focus on three new applications of Monte Carlo simulation in electricity price prediction, algorithm and health risk assessment. The limitations and future development of the Monte Carlo simulation are discussed later. Future research should solve the defects of Monte Carlo simulation with long computing consumption time, lack of evaluation method and strict sampling requirements, and enhance the adaptability of this method by combining the problems worth research in various fields. This paper hopes to provide the reader with the relevant background knowledge of Monte Carlo simulations to facilitate the application of Monte Carlo simulation to complex problems in more domains. Overall, these results shed light on guiding further exploration of applications based on Monte Carlo Simulations.

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6

SWENDSEN, 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.

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Although histogram methods have been extremely effective for analyzing data from Monte Carlo simulations, they do have certain limitations, including the range over which they are valid and the difficulties of combining data from independent simulations. In this paper, we describe a complementary approach to extracting information from Monte Carlo simulations that uses the matrix of transition probabilities. Combining the Transition Matrix with an N-fold way simulation technique produces an extremely flexible and efficient approach to rather general Monte Carlo simulations.

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7

Phoa, Wesley. "Conditional Monte Carlo Simulation." Journal of Investing 8, no. 3 (August 31, 1999): 80–88. http://dx.doi.org/10.3905/joi.1999.319371.

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8

Wang, Yazhen. "Quantum Monte Carlo simulation." Annals of Applied Statistics 5, no. 2A (June 2011): 669–83. http://dx.doi.org/10.1214/10-aoas406.

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9

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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10

Cheng, Minqi, and Jiasheng Guo. "Analysis of the Principle and Two Applications for Monte-Carlo Simulations." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 136–41. http://dx.doi.org/10.54097/3dg18k50.

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As a matter of fact, stochastic process and sampling algorithms are widely used in the state-of-art numerical simulations. In order to evaluate the random effect, the means of Monte-Carlo simulations are widely adopted and used to obtain a convergence or trending results. With this in mind, this essay mainly talks about the two applications of Monte Carlo simulation and the impact of it toward the society and human race. To be specific, firstly, the origin of Monte-Carlo simulation was revealed and its history of development was elaborated. After that, the basic concept of Monte-Carlo analysis was formulated as well as the sampling process of it is done briefly. All those foreshadows were aimed at assisting the readers to obtain a basic idea of this simulating method and be able to comprehend the relatively sophisticated applications, including financial and computer science knowledge. Overall, these results shed light on guiding further exploration of Monte-Carlo simulations.

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11

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.

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Lately, the Local Iterative Monte Carlo technique was introduced for an efficient simulation of effects connected to sparsely populated regions in semiconductor devices like hot electron effects in silicon MOSFETs. This paper focuses on computational aspects of this new Monte Carlo technique, namely the reduction of the computation time by parallel computation and the reuse of drift information. The Local Iterative Monte Carlo technique combines short Monte Carlo particle flight simulations with an iteration process to a complete device simulation. The separation between short Monte Carlo simulations and the iteration process makes a simple parallelization strategy possible. The necessary data transfer is small and can be performed via the Network File System. An almost linear speed up could be achieved. Besides by parallelization, the computational expenses can be significantly reduced, when the results of the short Monte Carlo simulations are memorized in a drift table and used several times. A comparison between a bulk, a one-dimensional and the two-dimensional Local Iterative Monte Carlo simulation reveals that by using the drift information more than once, becomes increasingly efficient with increasing dimension of the simulation.

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12

Caron, Pier-Olivier. "La loi généralisée de appariement: Une simulation de Monte-Carlo." ACTA COMPORTAMENTALIA 22, no. 2 (June 2014): 169–79. https://doi.org/10.32870/ac.v22i2.48904.

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La loi généralisée de l’appariement (LGA) est un modèle descriptif conceptualisant le ratio des réponses en fonction du ratio des renforçateurs (Baum, 1974). Les résultats des études montrent généralement une sen-sibilité autour de 0,80 et des variances expliquées (r2) supérieures à 0,80 (Davison & McCarthy, 1988). Les r2 très élevés de la LGA suggèrent la présence de contraintes inhérentes à la plupart des situations opérantes. Dans un programme de renforcement concurrent à intervalle variable, par exemple, la quantité de renfor-çateurs ne peut surpasser la quantité de comportements émis. L’objectif de la présente étude est de simuler les résultats de la LGA lorsque cette contrainte est implantée à des échantillons pseudoaléatoires. Au total, seize conditions sont répliquées 1 500 fois, représentant 24 000 essais. Les résultats montrent que la LGA obtient en moyenne un r2 de 47 %, une sensibilité de 0,60 et un biais nul. Les résultats suggèrent que des r2 de 0,62, 0,80 et 0,97 sont qualifiables, respectivement, de petite, moyenne et forte différence comparativement à 0,47. La perspective de la présente étude est comparée aux simulations de McDowell (2004) et des pistes de recherche futures sont présentées.

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13

Wang, Zijie. "Resarch of Monte-Carlo Simulation in Grain Growth." Journal of Physics: Conference Series 2133, no. 1 (November 1, 2021): 012014. http://dx.doi.org/10.1088/1742-6596/2133/1/012014.

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Abstract This paper is produced after writing code for doing Monte Carlo simulations of a single type and use the model to study the self-assembly of co-polymers confined to a surface. A great interest has been aroused in the field of Monte Carlo simulation in material science since then. The Monte Carlo algorithm for single-phase normal grain growth is realized which can simulate and observe the current development of the microstructure of large grains in three dimensions. And this study will go through both two- and three-dimension Monte Carlo simulation in grain growth with a brief introduction of the methodology about this. At last, an enormous potential of the Monte Carlo simulation could be spotted in material field and the future material analysis will rely more on computational science due to the powerful computing power.

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14

Huang, Xiangyuan. "Consumer and Marketing Research Using the Monte Carlo Simulation." Advances in Economics, Management and Political Sciences 32, no. 1 (November 10, 2023): 35–41. http://dx.doi.org/10.54254/2754-1169/32/20231561.

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In order to conduct consumer-related research and develop marketing strategies to outperform rival businesses, Monte Carlo simulation, a technique that was first employed in nuclear weapons and has subsequently been used in other physics-related domains, is described in this study. The literature on using Monte Carlo simulation for market and customer-related research and suggestions is summarized in two parts in this paper. The first section discusses the role of Monte Carlo simulations in customer research, outlining the various factors that affect consumers' decisions to purchase goods and services, and the second section discusses the specific help that Monte Carlo simulations can offer businesses, particularly in terms of measuring markets and creating effective marketing strategies. The paper also offers several applicable examples to describe certain elements in the middle of the text. Eventually, it is argued that Monte Carlo simulation, when used in conjunction with other techniques, can assist businesses in comprehending the market's costs and unpredictability and in developing effective marketing strategies.

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15

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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16

Mo, Zihan, Boxu He, and Tian Qin. "Option Pricing Based on Several Monte Carlo Techniques." Theoretical and Natural Science 107, no. 1 (May 6, 2025): 227–34. https://doi.org/10.54254/2753-8818/2025.22650.

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The Monte Carlo method is broadly used in financial technology and engineering for pricing complex derivatives and managing risk due to its flexibility and adaptability. However, Monte Carlo simulation may suffer from high variance problems, impacting accuracy and effectiveness. Control and antithetic variates are two main variance-reduction techniques to optimize the simulation. This paper compares the performance of normal Monte Carlo, and Monte Carlo optimized with control variates or antithetic variates in four different European options. In the work, the Monte Carlo optimization based on antithetic variates generally performs well, but in power options, the control variable method has a better effect on Monte Carlo optimization. By leveraging these variance reduction techniques, the accuracy and effectiveness of Monte Carlo simulations can be significantly enhanced, leading to more reliable option pricing. The results not only demonstrate the important role of variance-reduction techniques in the Monte Carlo method but also offer practical methods to improve option pricing strategy.

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17

Kiviet, Jan F. "Monte Carlo Simulation for Econometricians." Foundations and Trends® in Econometrics 5, no. 1-2 (2011): 1–181. http://dx.doi.org/10.1561/0800000011.

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18

Nakagawa, Kenji. "Basics of Monte Carlo Simulation." IEICE Communications Society Magazine 2008, no. 6 (2008): 6_11–6_20. http://dx.doi.org/10.1587/bplus.2008.6_11.

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19

Jaenisch, G. R., C. Bellon, M. Zhukovsky, and S. Podoliako. "Monte-Carlo-Simulation und CAD." Materials Testing 47, no. 4 (April 2005): 210–18. http://dx.doi.org/10.3139/120.100650.

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20

Creutz, Michael. "Microcanonical cluster Monte Carlo simulation." Physical Review Letters 69, no. 7 (August 17, 1992): 1002–5. http://dx.doi.org/10.1103/physrevlett.69.1002.

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21

Heringa, J. R., and H. W. J. Blöte. "Geometric cluster Monte Carlo simulation." Physical Review E 57, no. 5 (May 1, 1998): 4976–78. http://dx.doi.org/10.1103/physreve.57.4976.

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22

Wang, Jian-Sheng, and Robert H. Swendsen. "Replica Monte Carlo Simulation (Revisited)." Progress of Theoretical Physics Supplement 157 (2005): 317–23. http://dx.doi.org/10.1143/ptps.157.317.

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23

Uyeno, Dean. "Monte Carlo simulation on microcomputers." SIMULATION 58, no. 6 (June 1992): 418–23. http://dx.doi.org/10.1177/003754979205800611.

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24

Sheet, Abd Al Kareem I., and Nadia Adeel Saeed. "Monte Carlo Simulation and Applications." Journal of Kufa for Mathematics and Computer 1, no. 6 (December 30, 2012): 75–78. https://doi.org/10.31642/jokmc/2018/010608.

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The Monte Carlo simulation technique is one of the common computational tools used to imitate and follow up complex real life systems and their development with time. Variables of a disease problem were defined and the mathematical model for this problem was constructed. The numerical solution of this model was compared with the computational simulation of Â Markov Renewal Process of the type '' Birth and Death ''. We obvious from the results we obtained the efficiency of the Monte Carlo simulation technique and through out extended time periods episodes.Â

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25

Giles, Michael B. "Multilevel Monte Carlo Path Simulation." Operations Research 56, no. 3 (June 2008): 607–17. http://dx.doi.org/10.1287/opre.1070.0496.

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Schmidt, Rainer. "Monte Carlo simulation of bioadhesion." International Biodeterioration & Biodegradation 40, no. 1 (January 1997): 29–36. http://dx.doi.org/10.1016/s0964-8305(97)00059-0.

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Lipinski, Hans-Gerd, and Gerald Küther. "Monte-Carlo Simulation spinaler Motoneuronausfälle." Biomedizinische Technik/Biomedical Engineering 39, s1 (1994): 350–51. http://dx.doi.org/10.1515/bmte.1994.39.s1.350.

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Gardner, Robin P., and Lianyan Liu. "Monte Carlo simulation for IRRMA." Applied Radiation and Isotopes 53, no. 4-5 (November 2000): 837–55. http://dx.doi.org/10.1016/s0969-8043(00)00233-5.

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Pradlwarter, H. J., and G. I. Schuëller. "Local Domain Monte Carlo Simulation." Structural Safety 32, no. 5 (September 2010): 275–80. http://dx.doi.org/10.1016/j.strusafe.2010.03.009.

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Schulze, Tim P. "Efficient kinetic Monte Carlo simulation." Journal of Computational Physics 227, no. 4 (February 2008): 2455–62. http://dx.doi.org/10.1016/j.jcp.2007.10.021.

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Campostrini, Massimo, Paolo Rossi, and Ettore Vicari. "Monte Carlo simulation ofCPN−1models." Physical Review D 46, no. 6 (September 15, 1992): 2647–62. http://dx.doi.org/10.1103/physrevd.46.2647.

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de Groot, Paul F. M., Albertus H. Bril, Frans J. T. Floris, and A. Ewan Campbell. "Monte Carlo simulation of wells." GEOPHYSICS 61, no. 3 (May 1996): 631–38. http://dx.doi.org/10.1190/1.1443992.

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We present a method to simulate wells, i.e., 1-D stratigraphic profiles with attached physical properties but without spatial information, using a combination of geological knowledge and Monte Carlo statistics. The simulated data is intended to be used in seismic lateral prediction studies. Our algorithm simulates correlated stochastic variables one by one. There are two major advantages in this approach above the conventional way in which all correlated stochastic vectors are drawn simultaneously. The first advantage is that we can steer the algorithm with rules based on geological reasoning. The second advantage is that we can include hard constraints for each of the stochastic variables. If a simulated value does not satisfy these constraints, it can simply be drawn again. The input to the simulation algorithm consists of geological rules, probability density functions, correlations, and hard constraints for the stochastic variables. The variables are attached to the entities of a generic integration framework, which consists of acoustic‐stratigraphic units organized at three scale levels. The simulation algorithm constructs individual wells by selecting entities from the framework. The order in which the entities occur, and the thickness of each entity, is determined by a combination of random draws and specified geological rules. Acoustic properties and optional user‐defined physical properties are attached to the simulated layers by random draws. The acoustic properties are parameterized by top and bottom sonic and density values. The algorithm is capable of simulating acoustic hydrocarbon effects. The algorithm is demonstrated with a simulated example, describing the stratigraphic and physical variations in an oil field with a fluvial‐deltaic labyrinth type reservoir.

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Creutz, Michael. "Overrelaxation and Monte Carlo simulation." Physical Review D 36, no. 2 (July 15, 1987): 515–19. http://dx.doi.org/10.1103/physrevd.36.515.

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Förster, Stefan. "Monte Carlo-Simulation korrelierter Zufallsvariablen." Blätter der DGVFM 23, no. 3 (April 1998): 305–11. http://dx.doi.org/10.1007/bf02808293.

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Kuhl, Nelson M. "Monte Carlo Simulation of Transport." Journal of Computational Physics 129, no. 1 (November 1996): 170–80. http://dx.doi.org/10.1006/jcph.1996.0241.

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Stauffer, Dietrich. "Monte-Carlo-Simulation mikroskopischer Börsenmodelle." Physik Journal 55, no. 5 (May 1999): 49–51. http://dx.doi.org/10.1002/phbl.19990550511.

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Fujibuchi, Toshioh, and Akihiko Takahashi. "9. Application of the Monte Carlo Simulation 6: Monte Carlo Simulation in Nuclear Medicine." Japanese Journal of Radiological Technology 71, no. 5 (2015): 460–67. http://dx.doi.org/10.6009/jjrt.2015_jsrt_71.5.460.

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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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CODDINGTON, P. D. "ANALYSIS OF RANDOM NUMBER GENERATORS USING MONTE CARLO SIMULATION." International Journal of Modern Physics C 05, no. 03 (June 1994): 547–60. http://dx.doi.org/10.1142/s0129183194000726.

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Monte Carlo simulation is one of the main applications involving the use of random number generators. It is also one of the best methods of testing the randomness properties of such generators, by comparing results of simulations using different generators with each other, or with analytic results. Here we compare the performance of some popular random number generators by high precision Monte Carlo simulation of the 2-d Ising model, for which exact results are known, using the Metropolis, Swendsen-Wang, and Wolff Monte Carlo algorithms. Many widely used generators that perform well in standard statistical tests are shown to fail these Monte Carlo tests.

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Scott, John Henry J., Robert L. Myklebust, and Dale E. Newbury. "Parallel Monte Carlo Simulation Using Desktop Computers." Microscopy Today 8, no. 2 (March 2000): 34–35. http://dx.doi.org/10.1017/s1551929500057485.

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Monte Carlo simulation of electron scattering in solids has proven valuable to electron microscopists for many years. The electron trajectories, x-ray generation volumes, and scattered electron signals produced by these simulations are used in quantitative x-ray microanalysis, image interpretation, experimental design, and hypothesis testing. Unfortunately, these simulations are often computationally expensive, especially when used to simulate an image or survey a multidimensional region of parameter space.Here we present techniques for performing Monte Carlo simulations in parallel on a cluster of existing desktop computers. The simulation of multiple, independent electron trajectories in a sample and the collateral calculation of detected xray and electron signals fall into a class of computational problems termed “embarrassingly parallel”, since no information needs to be exchanged between parallel threads of execution during the calculation.

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Koh, Wook Hee. "Monte Carlo Simulation of Thermionic Low Pressure Discharge Plasma." Transactions of The Korean Institute of Electrical Engineers 61, no. 12 (December 1, 2012): 1880–85. http://dx.doi.org/10.5370/kiee.2012.61.12.1880.

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Takahashi, Akiyuki, Naoki Soneda, and Masanori Kikuchi. "Computer Simulation of Microstructure Evolution of Fe-Cu Alloy during Thermal Ageing." Key Engineering Materials 306-308 (March 2006): 917–22. http://dx.doi.org/10.4028/www.scientific.net/kem.306-308.917.

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This paper describes a computer simulation of thermal ageing process in Fe-Cu alloy. In order to perform accurate numerical simulation, firstly, we make numerical models of the diffusion and dissociation of Cu and Cu-vacancy clusters. This modeling was performed with kinetic lattice Monte Carlo method, which allows us to perform long-time simulation of vacancy diffusion in Fe-Cu dilute alloy. The model is input to the kinetic Monte Carlo method, and then, we performed the kinetic Monte Carlo simulation of the thermal ageing in the Fe-Cu alloy. The results of the KMC simulations tell us that the our new models describes well the rate and kinetics of the diffusion and dissociation of Cu and Cu-vacancy clusters, and works well in the kinetic Monte Carlo simulations. Finally, we discussed the further application of these numerical models.

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PUTRI, LUH HENA TERECIA WISMAWAN, KOMANG DHARMAWAN, and I. WAYAN SUMARJAYA. "PENENTUAN HARGA JUAL OPSI BARRIER TIPE EROPA DENGAN METODE ANTITHETIC VARIATE PADA SIMULASI MONTE CARLO." E-Jurnal Matematika 7, no. 2 (May 13, 2018): 71. http://dx.doi.org/10.24843/mtk.2018.v07.i02.p187.

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The purpose of this research is to compare the selling price of down and out barrier option when the prices are simulated by the Antithetic Variate Monte Carlo and the standar Monte Carlo. Barrier options are path dependent options and the payoff depend on whether the underlying asset price touched the barrier or not during the life of the option. In this research, we conducted simulations against the closing price of the shares of PT Adhi Karya using Standard Monte Carlo simulation and the Monte Carlo-Antithetic Variate simulation. After the simulation, we obtained that the option prices using Antithetic Variate produces a cheaper price than the standar one. We also found that the analytic solution has a smaller error on its confidence interval compare to the Monte Carlo Standar.

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Scott, John Henry J., Robert L. Myklebust, and Dale E. Newbury. "Parallel Monte Carlo Simulation Using Desktop Computers." Microscopy and Microanalysis 5, S2 (August 1999): 80–81. http://dx.doi.org/10.1017/s1431927600013726.

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Monte Carlo simulation of electron scattering in solids has proven valuable to electron microscopists for many years. The electron trajectories, x-ray generation volumes, and scattered electron signals produced by these simulations are used in quantitative x-ray microanalysis, image interpretation, experimental design, and hypothesis testing. Unfortunately, these simulations are often computationally expensive, especially when used to simulate an image or survey a multidimensional region of parameter space.Here we present techniques for performing Monte Carlo simulations in parallel on a cluster of existing desktop computers. The simulation of multiple, independent electron trajectories in a sample and the collateral calculation of detected x-ray and electron signals falls into a class of computational problems termed “embarrassingly parallel”, since no information needs to be exchanged between parallel threads of execution during the calculation. Such problems are ideally suited to parallel multicomputers, where a manager process distributes the computational burden over a large number of nodes.

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Danforth, Amanda L., and Lyle N. Long. "Nonlinear acoustic simulations using direct simulation Monte Carlo." Journal of the Acoustical Society of America 116, no. 4 (October 2004): 1948–55. http://dx.doi.org/10.1121/1.1785614.

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Br Manik, Mawar Bonita, Putri Khairiah Nasution, Suyanto Suyanto, and Maulida Yanti. "Kajian Metode Simulasi Monte Carlo." Journal of Mathematics, Computations and Statistics 7, no. 2 (September 26, 2024): 232–42. http://dx.doi.org/10.35580/jmathcos.v7i2.2994.

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The Monte Carlo Simulation Method is one of the forecasting methods that uses random numbers, specifically through the use of a Linear Congruential Generator and mathematical equations for prediction, forecasting, estimation, and risk analysis. The Monte Carlo Simulation Method with one iteration has a high level of accuracy, as evidenced by previous research. The more iterations used, the more accurate the forecasting results. Therefore, the author is interested in examining how well the Monte Carlo Simulation Method with N iterations performs in forecasting. The study of the Monte Carlo Simulation Method with N iterations will be conducted on the forecast of the number of visitors to Fort Rotterdam. The aim of this research is to determine the accuracy of the Monte Carlo Simulation Method with N iterations for forecasting the number of visitors to Fort Rotterdam. The MAPE values from 2013 to 2018 using the Monte Carlo Simulation Method with N iterations sequentially are 16%, 13%, 13%, 12%, 1008%, and 31%. The forecasting ability from 2013 to 2016 falls into the good category, the forecasting for 2017 falls into the poor category, and the forecasting for 2018 falls into the fair category.

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OKAMOTO, Kohta, Naoki TAKANO, and Yuta SHIMIZU. "F407 Practical Monte Carlo Simulation for Highly Non-Linear Problem." Proceedings of The Computational Mechanics Conference 2011.24 (2011): _F—60_—_F—61_. http://dx.doi.org/10.1299/jsmecmd.2011.24._f-60_.

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Koerkamp, Bas Groot, Theo Stijnen, Milton C. Weinstein, and M. G. Myriam Hunink. "The Combined Analysis of Uncertainty and Patient Heterogeneity in Medical Decision Models." Medical Decision Making 31, no. 4 (October 25, 2010): 650–61. http://dx.doi.org/10.1177/0272989x10381282.

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The analysis of both patient heterogeneity and parameter uncertainty in decision models is increasingly recommended. In addition, the complexity of current medical decision models commonly requires simulating individual subjects, which introduces stochastic uncertainty. The combined analysis of uncertainty and heterogeneity often involves complex nested Monte Carlo simulations to obtain the model outcomes of interest. In this article, the authors distinguish eight model types, each dealing with a different combination of patient heterogeneity, parameter uncertainty, and stochastic uncertainty. The analyses that are required to obtain the model outcomes are expressed in equations, explained in stepwise algorithms, and demonstrated in examples. Patient heterogeneity is represented by frequency distributions and analyzed with Monte Carlo simulation. Parameter uncertainty is represented by probability distributions and analyzed with 2nd-order Monte Carlo simulation (aka probabilistic sensitivity analysis). Stochastic uncertainty is analyzed with 1st-order Monte Carlo simulation (i.e., trials or random walks). This article can be used as a reference for analyzing complex models with more than one type of uncertainty and patient heterogeneity.

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Fotr, Jiří, Lenka Švecová, Ivan Souček, and Lubomír Pešák. "Monte Carlo Simulation in Risk Analysis of Investment Projects." Acta Oeconomica Pragensia 15, no. 2 (April 1, 2007): 32–43. http://dx.doi.org/10.18267/j.aop.47.

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Weber, S., and H. Briesen. "Simulation der Stärkehydrolyse mittels kinetischer Monte-Carlo-Simulationen." Chemie Ingenieur Technik 84, no. 8 (July 25, 2012): 1207. http://dx.doi.org/10.1002/cite.201250188.

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