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Journal articles on the topic 'Monte Carlo Simulation Technique'

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Published: 4 June 2021
Last updated: 9 June 2025

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1

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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2

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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3

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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4

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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5

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  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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6

Suzuki, 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_.

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7

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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8

Nur, Nabihah Rusyda Roslan, Farhanie Mohd Fauzi NoorFatin, and Ikhwan Muhammad Ridzuan Mohd. "Variance reduction technique in reliability evaluation for distribution system by using sequential Monte Carlo simulation." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3061~3068. https://doi.org/10.11591/eei.v11i6.3950.

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This paper discusses the need for variance reduction in simulations in order to reduce the time required to compute a simulation. The large and complex network is commonly evaluated using a large-scale Monte Carlo simulation. Unfortunately, due to the different sizes of the network, it takes some time to complete a simulation. However, variance reduction techniques (VRT) can help to solve the issues. The effect of VRT changes the behaviors of a simulation, particularly the time required to run the simulation. To evaluate the reliability indices, two sequential Monte Carlo (SMC) methods are used. SMC with VRT and SMC without VRT are the two options. The presence of VRT in the simulation distinguishes the two simulations. Finally, reliability indices: system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and customer average interruption duration index (CAIDI) will be calculated at the end of the simulation to determine the efficiency for the SMC with and without VRT. Overall, the SMC with VRT is more efficient because it is more convenient and saves time than the SMC without VRT.
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9

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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10

Tamošiūnienė, Rima. "The Use of Monte Carlo Simulation Technique to Support Investment Decisions." Business: Theory and Practice 7, no. (2) (June 27, 2006): 73–80. https://doi.org/10.3846/btp.2006.09.

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In this paper the methodology and uses of Monte Carlo simulation technique in the evaluation of investment projects are presented to analyse and assess the risk. The first part presents the theoretical background of Monte Carlo analysis in various process stages. The second part examines the interpretation of the results generated by the application of project formulation, financial and risk analysis including investment decision criteria and measures of risk based on the expected NPV value concept. The final part draws some conclusions regarding the usefulness and limitations of Monte Carlo analysis in investment appraisal.
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11

Maazi, N. "Conversion of Monte Carlo Steps to Real Time for Grain Growth Simulation." Advances in Mathematical Physics 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4023470.

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Monte Carlo (MC) technique is becoming a very effective simulation method for prediction and analysis of the grain growth kinetics at mesoscopic level. It should be noted that MC models have no real time of physical systems due to the probabilistic nature of this simulation technique. This leads to difficulties when converting simulated time, the Monte Carlo steps tMCS, to real time. The correspondence between Monte Carlo steps and real time should be proposed for comparing the kinetics of MC models with the experiments. In this work, the conversion of Monte Carlo steps to real time is attempted. The lattice sites spacing Δ and the temperature T cannot be ignored in the Monte Carlo simulation of grain growth. Real time will be associated with tMCS, T, and Δ.
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12

Marjanovic, 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.

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In this paper we present the results of Monte Carlo simulations of positronium (Ps) swarm thermalization in helium (He) and water vapour. We have investigated the temporal evolution of energy and spatial parameters of the swarm and their sensitivity to the shape of the cross-section and the initial energy distribution. Positron anihilation spectroscopy (PAS) and positron emission tomography (PET) are techniques that depend on anihilation of positronium in materials and tissue. The results obtained point that the Monte Carlo technique shows good agreement with experimental results and is capable of accurately describing the behaviour of Ps particles including the energy, particle lifetime and the moment and location of the anihilation.
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13

Sherniyozov, 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.

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In search of high accuracy and convenient implementation, computational optics uses several modeling techniques such as Maxwell’s equations-based methods, ray-tracing method, Fourier optics methods and many others. Each with its advantages, disadvantages, and applicability caveats. In this paper, we describe Monte Carlo photon tracing as a simulation technique for computational optics. The validity of the main assumptions in the simulation method is demonstrated with examples, and limitations of method’s applicability are discussed.
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14

Nedjalkov, 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.

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15

Juang, 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.

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16

Fauzi, 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.

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17

Kosina, 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.

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18

Letosa, 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.

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19

Rusyda Roslan, Nur Nabihah, NoorFatin Farhanie Mohd Fauzi, and Mohd Ikhwan Muhammad Ridzuan. "Variance reduction technique in reliability evaluation for distribution system by using sequential Monte Carlo simulation." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3061–68. http://dx.doi.org/10.11591/eei.v11i6.3950.

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This paper discusses the need for variance reduction in simulations in order to reduce the time required to compute a simulation. The large and complex network is commonly evaluated using a large-scale Monte Carlo simulation. Unfortunately, due to the different sizes of the network, it takes some time to complete a simulation. However, variance reduction techniques (VRT) can help to solve the issues. The effect of VRT changes the behaviors of a simulation, particularly the time required to run the simulation. To evaluate the reliability indices, two sequential Monte Carlo (SMC) methods are used. SMC with VRT and SMC without VRT are the two options. The presence of VRT in the simulation distinguishes the two simulations. Finally, reliability indices: system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and customer average interruption duration index (CAIDI) will be calculated at the end of the simulation to determine the efficiency for the SMC with and without VRT. Overall, the SMC with VRT is more efficient because it is more convenient and saves time than the SMC without VRT.
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20

Sim, Hayoung, and Hee Seo. "Improving Computational Efficiency in Monte Carlo Simulation for Radiation Portal Monitor." EPJ Web of Conferences 302 (2024): 04005. http://dx.doi.org/10.1051/epjconf/202430204005.

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At the border and airports, a radiation portal monitor (RPM) system has been installed and is operated to detect illegal radioactive materials in cargo containers. To evaluate the RPM's performance and determine the optimized alarm criteria in various conditions, the Monte Carlo simulation technique is considered essential; however, the calculation efficiency has to be improved when we are dealing with complex geometries and a wide range of source term (e.g., a few hundred meters in diameter). In the present study, the applicability of the various techniques to improve calculation efficiency was evaluated. For example, as a simple but highly effective way, a simple box was used to surround the complex geometry of the RPM, resulting in 1.75 times of improvement in the calculation efficiency. Additionally, when the simple box and Surface Source Write and Read (SSW/SSR) techniques were combined, the computational efficiency increased by 374 times. These results could significantly enhance the calculation efficiency of the Monte Carlo simulation, not only for the RPM but also for other radiation detectors in radiation transport applications.
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21

Muhammad, Wazir, Ying Liang, Gregory R. Hart, Bradley J. Nartowt, and Jun Deng. "Monte Carlo simulation of coherently scattered photons based on the inverse-sampling technique." Acta Crystallographica Section A Foundations and Advances 76, no. 1 (January 1, 2020): 70–78. http://dx.doi.org/10.1107/s2053273319014530.

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The acceptance–rejection technique has been widely used in several Monte Carlo simulation packages for Rayleigh scattering of photons. However, the models implemented in these packages might fail to reproduce the corresponding experimental and theoretical results. The discrepancy is attributed to the fact that all current simulations implement an elastic scattering model for the angular distribution of photons without considering anomalous scattering effects. In this study, a novel Rayleigh scattering model using anomalous scattering factors based on the inverse-sampling technique is presented. Its performance was evaluated against other simulation algorithms in terms of simulation accuracy and computational efficiency. The computational efficiency was tested with a general-purpose Monte Carlo package named Particle Transport in Media (PTM). The evaluation showed that a Monte Carlo model using both atomic form factors and anomalous scattering factors for the angular distribution of photons (instead of the atomic form factors alone) produced Rayleigh scattering results in closer agreement with experimental data. The comparison and evaluation confirmed that the inverse-sampling technique using atomic form factors and anomalous scattering factors exhibited improved computational efficiency and performed the best in reproducing experimental measurements and related scattering matrix calculations. Furthermore, using this model to sample coherent scattering can provide scientific insight for complex systems.
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22

Yaroslavsky, Anna N., Amy F. Juliano, Ather Adnan, Wayne J. Selting, Tyler W. Iorizzo, James D. Carroll, Stephen T. Sonis, Christine N. Duncan, Wendy B. London, and Nathaniel S. Treister. "Validation of a Monte Carlo Modelling Based Dosimetry of Extraoral Photobiomodulation." Diagnostics 11, no. 12 (November 26, 2021): 2207. http://dx.doi.org/10.3390/diagnostics11122207.

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An in vivo validation study was performed to confirm the accuracy of extraoral photobiomodulation therapy (PBMT) dosimetry determined by modelling. The Monte Carlo technique was utilized to calculate the fluence rate and absorbed power of light delivered through multi-layered tissue. Optical properties used during Monte Carlo simulations were taken from the literature. Morphological data of four study volunteers were acquired using magnetic resonance imaging (MRI) scans. Light emitting diode (LED) coupled to a power meter were utilized to measure transmitted power through each volunteer’s cheek, in vivo. The transmitted power determined by Monte Carlo modelling was compared to the in vivo measurements to determine the accuracy of the simulations. Experimental and simulation results were in good agreement for all four subjects. The difference between the mean values of the measured transmission was within 12% from the respective transmission obtained using Monte Carlo simulations. The results of the study indicate that Monte Carlo modelling is a robust and reliable method for light dosimetry.
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23

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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24

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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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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26

Meglinski, Igor V., Vladimir L. Kuzmin, Dmitry Y. Churmakov, and Douglas A. Greenhalgh. "Monte Carlo simulation of coherent effects in multiple scattering." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 461, no. 2053 (January 8, 2005): 43–53. http://dx.doi.org/10.1098/rspa.2004.1369.

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Using a combination of the stochastic Monte Carlo technique and the iteration procedure of the solution to the Bethe–Salpeter equation, it has been shown that the simulation of the optical path of a photon packet undergoing an n th scattering event directly corresponds to the n th–order ladder diagram contribution. In this paper, the Monte Carlo technique is generalized for the simulation of the coherent back–scattering and temporal correlation function of optical radiation scattered within the randomly inhomogeneous turbid medium. The results of simulation demonstrate a good agreement with the diffusing wave theory and experimental results.
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İnanç, Şahin, Onur Mesut Şenaras, Arzu Eren Şenaras, and Burcu Öngen Bilir. "Monte Carlo Simulation Via Visual Basic Application." Revista de Gestão Social e Ambiental 18, no. 10 (October 22, 2024): e09063. http://dx.doi.org/10.24857/rgsa.v18n10-278.

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Objective: The objective of this study is to implement the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. Theoretical Framework: Implementing the results of the changes we desire on a real-life system through trial and error can lead to costly and risky outcomes. Simulation allows us to develop a computer model of an existing system, enabling us to understand the system's behavior. Method: Monte Carlo Simulation, a widely used simulation technique, utilizes random sampling to estimate possible outcomes of a process or experiment. Monte Carlo Simulation utilizes random sampling to estimate the outcomes of an experiment. Results and Discussion: The purpose of this study is to implement the Monte Carlo Simulation method using MS Excel Visual Basic Application. When the user interacts with the Excel interface by clicking the "Calculate" button, the system generates random numbers based on predefined probability distributions. Subsequently, statistical measures such as the mean, variance, standard deviation, median, mode, range, and extreme values (maximum and minimum) are calculated using VBA. Research Implications: Thanks to the developed application, Monte Carlo Simulation applications can be calculated easily. Originality/Value: This study contributes to the literature by implementing the Monte Carlo Simulation method using Microsoft Excel's Visual Basic for Applications (VBA). This approach enables users to simulate complex systems easily and obtain statistical outputs for decision-making. These statistics provide insights into the underlying randomness and variability of the system, which aids in decision-making processes.
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Hu, S. L. J., and M. Muin. "Dynamic Response Statistics of Linear System Under Morison-Type Wave Force." Journal of Offshore Mechanics and Arctic Engineering 112, no. 1 (February 1, 1990): 42–52. http://dx.doi.org/10.1115/1.2919834.

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The primary objective of this study is to establish the non-normal relationship between Morison-type random wave loading and structural response via repeated Monte Carlo simulations. A standardized equation of motion will be developed, such that the numerical results can apply to various structures and sea states, instead of a particular structure or sea state. The reliability of the Monte Carlo simulation technique will also be investigated, analytically and experimentally.
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Santos, T. A., and C. Guedes Soares. "Monte Carlo simulation of damaged ship survivability." Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 219, no. 1 (March 1, 2005): 25–35. http://dx.doi.org/10.1243/147509005x10404.

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A probabilistic methodology for assessing damaged ship survivability based on Monte Carlo simulation is proposed. The damage condition is generated using statistical distributions of damage location and extent, derived during a recent research project, coupled to an algorithm that identifies the damaged compartments. Survivability of the ship in each damage condition is evaluated using the formulae recently proposed in Report SLF 46, which are partly based on the static equivalent method and partly on a more conventional approach. The Monte Carlo simulation technique is applied taking into consideration random quantities related to the damage and loading conditions at the time of accident. This methodology is applied to a passenger roll-on roll-off ship, allowing for a direct estimate of the damaged ship survivability in terms of the number of simulated ship capsizes. The effects of changes in the vertical position of the centre of gravity and in the sea state distribution at the moment of accident are also evaluated.
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ZHENG, QIAN, and XIANGPENG LI. "GAS DIFFUSION COEFFICIENT OF FRACTAL POROUS MEDIA BY MONTE CARLO SIMULATIONS." Fractals 23, no. 02 (May 28, 2015): 1550012. http://dx.doi.org/10.1142/s0218348x15500127.

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Gas diffusion behavior in fractal porous media is simulated by Monte Carlo technique in this work. Based on the fractal character of pore size in porous media, the probability model of the effective gas diffusion coefficient is derived. The proposed model of the effective gas diffusion coefficient is explicitly expressed as a function of structural parameters of porous media, such as porosity, pore size, the fractal dimensions for pore area and tortuosity. The effect of structural parameters of porous media has been studied in detail. The results show that the present results from the Monte Carlo simulations present a good agreement with those from the available analytical model and the available experimental data. The proposed Monte Carlo simulation technique may have the potential in predictions of other gas transport properties in fractal porous media.
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Nguyễn, Duy Duẩn, Xuân Thục Phan та Trọng Hà Nguyễn. "Đánh giá độ tin cậy của dầm thép trên nền đàn hồi chịu tải trọng di động sử dụng mô phỏng monte carlo và phương pháp lấy mẫu HYPERCUBE LATIN". Vietnam Institute for Building Science and Technology 2023, vi.vol3 (жовтень 2023): 12–19. http://dx.doi.org/10.59382/j-ibst.2023.vi.vol3-2.

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Steel beams resting on the elastic foundation are an applied technique to minimize the impact of thermal expansion and vibrations on civil engineering structures such as bridges and buildings, due to their flexibility and ability to withstand large loads. In practice, the performance of steel beams resting on elastic foundations is affected by random factors such as exceeding load-bearing capacity, undesirable deformations, loss of soil resilience, or environmental corrosion. This study proposes a technique to evaluate the reliability of steel beams resting on the elastic foundation subjected to moving loads using discrete random input parameters through the Latin hypercube sampling and Monte Carlo simulation method. The reliability of steel beams obtained from the proposed method is then compared to that of the traditional Monte Carlo simulation. The results reveal that the proposed technique provides a high accuracy and computational resource savings compared to the traditional Monte Carlo method. Finally, the influence of the random input parameters is also assessed based on the Sobol' sensitivity index.
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32

Ferreiro-Castilla, Albert, and Kees van Schaik. "Applying the Wiener-Hopf Monte Carlo Simulation Technique for Lévy Processes to Path Functionals." Journal of Applied Probability 52, no. 1 (March 2015): 129–48. http://dx.doi.org/10.1239/jap/1429282611.

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In this paper we apply the recently established Wiener-Hopf Monte Carlo simulation technique for Lévy processes from Kuznetsov et al. (2011) to path functionals; in particular, first passage times, overshoots, undershoots, and the last maximum before the passage time. Such functionals have many applications, for instance, in finance (the pricing of exotic options in a Lévy model) and insurance (ruin time, debt at ruin, and related quantities for a Lévy insurance risk process). The technique works for any Lévy process whose running infimum and supremum evaluated at an independent exponential time can be sampled from. This includes classic examples such as stable processes, subclasses of spectrally one-sided Lévy processes, and large new families such as meromorphic Lévy processes. Finally, we present some examples. A particular aspect that is illustrated is that the Wiener-Hopf Monte Carlo simulation technique (provided that it applies) performs much better at approximating first passage times than a ‘plain’ Monte Carlo simulation technique based on sampling increments of the Lévy process.
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33

Ferreiro-Castilla, Albert, and Kees van Schaik. "Applying the Wiener-Hopf Monte Carlo Simulation Technique for Lévy Processes to Path Functionals." Journal of Applied Probability 52, no. 01 (March 2015): 129–48. http://dx.doi.org/10.1017/s0021900200012249.

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In this paper we apply the recently established Wiener-Hopf Monte Carlo simulation technique for Lévy processes from Kuznetsov et al. (2011) to path functionals; in particular, first passage times, overshoots, undershoots, and the last maximum before the passage time. Such functionals have many applications, for instance, in finance (the pricing of exotic options in a Lévy model) and insurance (ruin time, debt at ruin, and related quantities for a Lévy insurance risk process). The technique works for any Lévy process whose running infimum and supremum evaluated at an independent exponential time can be sampled from. This includes classic examples such as stable processes, subclasses of spectrally one-sided Lévy processes, and large new families such as meromorphic Lévy processes. Finally, we present some examples. A particular aspect that is illustrated is that the Wiener-Hopf Monte Carlo simulation technique (provided that it applies) performs much better at approximating first passage times than a ‘plain’ Monte Carlo simulation technique based on sampling increments of the Lévy process.
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34

Castanier, M. P., G. O´ttarsson, and C. Pierre. "A Reduced Order Modeling Technique for Mistuned Bladed Disks." Journal of Vibration and Acoustics 119, no. 3 (July 1, 1997): 439–47. http://dx.doi.org/10.1115/1.2889743.

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The analysis of the response statistics of mistuned turbomachinery rotors requires an expensive Monte Carlo simulation approach. Simple lumped parameter models capture basic localization effects but do not represent well actual engineering structures without a difficult parameter identification. Current component mode analysis techniques generally require a minimum number of degrees of freedom which is too large for running Monte Carlo simulations at a reasonable cost. In the present work, an order reduction method is introduced which is capable of generating reasonably accurate, very low order models of tuned or mistuned bladed disks. This technique is based on component modes of vibration found from a finite element analysis of a single disk-blade sector. It is shown that the phenomenon of mode localization is well captured by the reduced order modeling technique.
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35

Hasibuan, Lilis Harianti, and Rani Kurnia Putri. "Ekspektasi Maksimum Percentage Drawdown pada data Saham PT. Mayora Tbk dengan simulasi Monte Carlo." JOSTECH: Journal of Science and Technology 1, no. 1 (March 31, 2021): 91–104. http://dx.doi.org/10.15548/jostech.v1i1.2440.

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A drawdown is a tool for defining trading strategies for commodities, stocks, and investments. This analysis is one way of monitoring the decline in asset value over a certain period of time. This journal will discuss PT.MayoraTbk stock trading strategy. By analyzing the observed drawdown in the specified time period. The drawdown analysis here uses the feedback control on PT.MayoraTbk stock trading is assumed to follow the geometric Brownian motion. The data obtained is tested whether the data meets Brown's motion assumptions. Then the maximum drawdown expectation is determined at the selected time interval. An estimate is carried out for the maximum expected drawdown percentage of the share value. To test the validity of the estimation results, a Monte Carlo simulation is carried out. Monte Carlo simulation with the term Sampling Simulation or Monte Carlo Sampling Technique. This simulation sampling illustrates the possible use of sample data using the Monte Carlo method and also the distribution can be known or estimated. This simulation uses existing data (historical data) that is actually used in a simulation that includes inventory or sampling with a known and determined probability distribution, so this Monte Carlo simulation can be used. The basic idea of this Monte Carlo simulation is to generate or generate a value to form a model of the variables and study it.
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36

Koura, Katsuhisa. "Null-collision technique in the direct-simulation Monte Carlo method." Physics of Fluids 29, no. 11 (1986): 3509. http://dx.doi.org/10.1063/1.865826.

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37

Jakumeit, J., and U. Ravaioli. "Semiconductor transport simulation with the local iterative Monte Carlo technique." IEEE Transactions on Electron Devices 48, no. 5 (May 2001): 946–55. http://dx.doi.org/10.1109/16.918243.

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38

Trubitsyn, Andrey, and Victor Gurov. "Monte Carlo Technique of Simulation of Electron Motion in Gas." Microscopy and Microanalysis 21, S4 (June 2015): 258–63. http://dx.doi.org/10.1017/s1431927615013471.

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39

Passos, André Luis, Douglas F. de Albuquerque, and João Batista Santos Filho. "Representation and simulation for pyrochlore lattice via Monte Carlo technique." Physica A: Statistical Mechanics and its Applications 450 (May 2016): 541–45. http://dx.doi.org/10.1016/j.physa.2016.01.040.

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40

Kuznetsov, A., A. E. Kyprianou, J. C. Pardo, and K. van Schaik. "A Wiener–Hopf Monte Carlo simulation technique for Lévy processes." Annals of Applied Probability 21, no. 6 (December 2011): 2171–90. http://dx.doi.org/10.1214/10-aap746.

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41

Chen, Gang, and Iain D. Boyd. "Statistical Error Analysis for the Direct Simulation Monte Carlo Technique." Journal of Computational Physics 126, no. 2 (July 1996): 434–48. http://dx.doi.org/10.1006/jcph.1996.0148.

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42

Mohd Fauzi, NoorFatin Farhanie, Nur Nabihah Rusyda Roslan, and Mohd Ikhwan Muhammad Ridzuan. "RELIABILITY ASSESSMENT ON NETWORK EQUIVALENT USING MONTE-CARLO SIMULATION TECHNIQUE." Platform : A Journal of Engineering 4, no. 2 (June 30, 2020): 76. http://dx.doi.org/10.61762/pajevol4iss2art7542.

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Reliability in an electrical power system is the ability of the system to deliver electrical energy without any interruption. In the literature review, the analysis of the power system in low voltage (LV) network is less focused compared to the medium voltage (MV) and high voltage (HV) due to the absence of general exact data and sizing of the LV network. The LV network becomes complex when the sizing of the network increases. Thus, in this research, the performance of reliability in the LV network will be evaluated using an equivalent network by simplifying the network. This simplified network will reduce the simulation time. The performance in a complex network should first be evaluated before reliability assessments are carried out. The assessment of reliability is carried out using Monte-Carlo Simulation. The reliability indices, SAIFI, SAIDI and CAIDI between these two networks (detailed and equivalents network) are compared. The expected output in the detailed network will have the same or close readings of reliability indices as in an equivalent network.Keywords: low voltage, medium voltage, reliability. failure rate, repair time, interruptions
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43

Swain, M., Prasanta Kumar Rath, Balaji Padhy, Aditya Kumar Pati, Vaishali R. Patel, Nirali Gondaliya, Ami N. Deshmukh, Ravindra Prajapati, and N. N. Deshmukh. "Understanding The Projectile Breakup Mechanism Using Monte Carlo Simulation Technique." East European Journal of Physics, no. 4 (November 18, 2024): 195–99. https://doi.org/10.26565/2312-4334-2024-4-18.

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A montecarlo modeling simulation has been performed to understand the breakup bellow the barrier and for 7Li+208Pb reaction. A wider detection cone has been found for near target breakup compaired to assomptotic breakup. The exclusive coincidence particle spectrum for alpha and triton has also simulated and found the breakup from difeerent resonant state. The data has also represented.A classical approach has been adopted to understand the reaction meachanism.
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44

Erfanian, Hamid Reza, Seyed Jaliledin Ghaznavi Bidgoli, and Parvin Shakibaei. "The pricing of spread option using simulation." International Journal of Applied Mathematical Research 6, no. 4 (October 19, 2017): 121. http://dx.doi.org/10.14419/ijamr.v6i4.7914.

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Monte Carlo simulation is one of the most common and popular method of options pricing. The advantages of this method are being easy to use, suitable for all kinds of standard and exotic options and also are suitable for higher dimensional problems. But on the other hand Monte Carlo variance convergence rate is which due to that it will have relatively slow convergence rate to answer the problems, as to achieve accuracy when it has been d-dimensions, complexity is . For this purpose, several methods are provided in quasi Monte Carlo simulation to increase variance convergence rate as variance reduction techniques, so far. One of the latest presented methods is multilevel Monte Carlo that is introduced by Giles in 2008. This method not only reduces the complexity of computing amount in use of Euler discretization scheme and the amount in use of Milstein discretization scheme, but also has the ability to combine with other variance reduction techniques. In this paper, using Multilevel Monte Carlo method by taking Milstein discretization scheme, pricing spread option and compared complexity of computing with standard Monte Carlo method. The results of Multilevel Monte Carlo method in pricing spread options are better than standard Monte Carlo simulation.
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45

Zammataro, Matteo, Matteo Falabino, Nicolò Abrate, Mario Carta, and Daniele Tomatis. "A weight window approach for dose rate calculations in Lead-cooled Fast Reactors with OpenMC." EPJ Web of Conferences 302 (2024): 15003. http://dx.doi.org/10.1051/epjconf/202430215003.

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This paper presents the application of a variance reduction technique for radiation shielding calculations in Lead-cooled Fast Reactors (LFRs) using the OpenMC Monte Carlo code. The study focuses on assessing the computational efficiency of the Method of Automatic Generation of Importances by Calculation (MAGIC). This technique is employed to generate weight windows for Monte Carlo simulations, aiming at enhancing the efficiency of dose rate estimations for LFRs. Through a detailed study of a LFR mock-up model, the performance differences between a MAGIC-accelerated and a non-accelerated simulation are investigated in terms of both accuracy and computational cost. The results demonstrate the benefits of the variance reduction method, showing its effectiveness in spreading particles throughout the model while improving the accuracy of Monte Carlo estimates far from the core. In addition, this work demonstrates the performances of the MAGIC method in eigenvalue calculations. This work shows an interesting optimisation of Monte Carlo simulations for LFRs and can be considered as a first milestone for newcleo’s radiation shielding studies.
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46

Ruhiyat, Ruhiyat, Berlian Setiawaty, and Muwafiqo Zamzami Dhuha. "Value at Risk dan Tail Value at Risk dari Peubah Acak Besarnya Kerugian yang Menyebar Alpha Power Pareto." Jambura Journal of Mathematics 5, no. 1 (January 18, 2023): 67–82. http://dx.doi.org/10.34312/jjom.v5i1.16586.

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Value at Risk (VaR) and Tail Value at Risk (TVaR) are two measures that are commonly used to quantify the risk associated with a loss severity distribution. In this paper, both values are calculated analytically and estimated using a Monte Carlo simulation when the loss severity random variable has an alpha power Pareto distribution. This distribution is the result of alpha power transformation on a Pareto distribution. The random numbers used in the Monte Carlo simulation are generated from the alpha power Pareto distribution using the inverse transformation technique. In the special case used, the estimated VaR and TVaR values obtained from the Monte Carlo simulation for some security levels used are close to the actual VaR and TVaR values as long as the number of random numbers generated in the Monte Carlo simulation is sufficiently large.
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47

Quesada, Henry, Sailesh Adhikari, Brian Bond, and Shawn T. Grushecky. "Analysis of hardwood lumber grade yields using Monte Carlo simulation." BioResources 14, no. 1 (January 25, 2019): 2029–50. http://dx.doi.org/10.15376/biores.14.1.2029-2050.

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The goal of this study was to develop a lumber grade yield prediction model with a probability-based technique known as the Monte Carlo simulation. The data to develop the prediction model was taken from an existing lumber grade yield database developed from red oak logs sawn at the Appalachian region of the United States. Statistical input analysis techniques were used to fit the lumber grade yields to hypothesized probability distributions. Inverse cumulative probability function distributions were developed from the fitted probability distributions to simulate and predict lumber grade yields. The predicted gross revenue was compared with the actual gross revenue and against the gross revenue predicted by a multiple linear regression (MLR) model. The predicted gross revenue using the Monte Carlo simulation had a 0.88% absolute error compared with the actual gross revenue, while the predicted gross revenue from the MLR model had an absolute error of 3.31%. The higher prediction power of the Monte Carlo method was more effective when predicting lumber grade yields from individual log groups. The Monte Carlo model developed in this research can be easily implemented to quickly predict lumber grade yields or gross revenue to support procurement, log inventory management, production, planning, and marketing operations.
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48

Karatepe, Selin, and Kenneth W. Corscadden. "Wind Speed Estimation: Incorporating Seasonal Data Using Markov Chain Models." ISRN Renewable Energy 2013 (December 18, 2013): 1–9. http://dx.doi.org/10.1155/2013/657437.

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This paper presents a novel approach for accurately modeling and ultimately predicting wind speed for selected sites when incomplete data sets are available. The application of a seasonal simulation for the synthetic generation of wind speed data is achieved using the Markov chain Monte Carlo technique with only one month of data from each season. This limited data model was used to produce synthesized data that sufficiently captured the seasonal variations of wind characteristics. The model was validated by comparing wind characteristics obtained from time series wind tower data from two countries with Markov chain Monte Carlo simulations, demonstrating that one month of wind speed data from each season was sufficient to generate synthetic wind speed data for the related season.
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49

MENDEŞ, Mehmet. "Re-evaluating the Monte Carlo Simulation Results by Using Graphical Techniques." Turkiye Klinikleri Journal of Biostatistics 13, no. 1 (2021): 28–38. http://dx.doi.org/10.5336/biostatic.2020-78896.

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50

Khu, S. T., and M. G. F. Werner. "Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling." Hydrology and Earth System Sciences 7, no. 5 (October 31, 2003): 680–92. http://dx.doi.org/10.5194/hess-7-680-2003.

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Abstract. Monte-Carlo (MC) simulation based techniques are often applied for the estimation of uncertainties in hydrological models due to uncertain parameters. One such technique is the Generalised Likelihood Uncertainty Estimation technique (GLUE). A major disadvantage of MC is the large number of runs required to establish a reliable estimate of model uncertainties. To reduce the number of runs required, a hybrid genetic algorithm and artificial neural network, known as GAANN, is applied. In this method, GA is used to identify the area of importance and ANN is used to obtain an initial estimate of the model performance by mapping the response surface. Parameter sets which give non-behavioural model runs are discarded before running the hydrological model, effectively reducing the number of actual model runs performed. The proposed method is applied to the case of a simple two-parameter model where the exact parameters are known as well as to a widely used catchment model where the parameters are to be estimated. The results of both applications indicated that the proposed method is more efficient and effective, thereby requiring fewer model simulations than GLUE. The proposed method increased the feasibility of applying uncertainty analysis to computationally intensive simulation models. Keywords: parameters, calibration, GLUE, Monte-Carlo simulation, Genetic Algorithms, Artificial Neural Networks, hydrological modelling, Singapore
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