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Journal articles on the topic 'Monte Carlo Simulation Technique'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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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

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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3

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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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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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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6

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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7

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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8

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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9

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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10

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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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

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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13

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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14

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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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

Yang, Zhiyu. "Application of Monte-Carlo Simulations in Environment and Chemistry." Highlights in Science, Engineering and Technology 17 (November 10, 2022): 5–11. http://dx.doi.org/10.54097/hset.v17i.2430.

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Contemporarily, with the rapid development of the computer science, the calculation ability has been boosted a lot, which paves a path for carrying out simulations with random variable. In this case, Monte-Carlo simulations can be easily achieved with the state-of-art techniques. As one of the useful statistical tools, Monte-Calo simulation can be implemented in different areas including environment and Chemistry field. On this basis, this article searched several information to determine the usage of the simulation in the certain field. To be specific, the usage of Monte-Calo simulations of Chemistry and Environmental Science will be discussed with specific samples and data analysis. Moreover, relevant simulations results and corresponding applications will be demonstrated. In addition, this study will also summarize some limitations (e.g., the large range of the data). On this basis, it provides some suggests to elaborate the function in more digits and stating the conclusion. These results shed light on guiding further simulations in the context of the Monte-Carolo configuration.

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29

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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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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31

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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32

APAJA, VESA, and OLAV F. SYLJUÅSEN. "MONTE CARLO SIMULATION OF BOSON LATTICES." International Journal of Modern Physics B 20, no. 30n31 (December 20, 2006): 5113–16. http://dx.doi.org/10.1142/s0217979206036168.

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Boson lattices are theoretically well described by the Hubbard model. The basic model and its variants can be effectively simulated using Monte Carlo techniques. We describe two newly developed approaches, the Stochastic Series Expansion (SSE) with directed loop updates and continuous–time Diffusion Monte Carlo (CTDMC). SSE is a formulation of the finite temperature partition function as a stochastic sampling over product terms. Directed loops is a general framework to implement this stochastic sampling in a non–local fashion while maintaining detailed balance. CTDMC is well suited to finding exact ground–state properties, applicable to any lattice model not suffering from the sign problem; for a lattice model the evolution of the wave function can be performed in continuous time without any time discretization error. Both the directed loop algorithm and the CTDMC are important recent advances in development of computational methods. Here we present results for a Hubbard model for anti–ferromagnetic spin–1 bosons in one dimensions, and show evidence for a dimerized ground state in the lowest Mott lobe.

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33

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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34

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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35

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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36

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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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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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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Tamošiūnienė, Rima, and Tomas Petravičius. "The Use of Monte Carlo Simulation Technique to Support Investment Decisions." Verslas: teorija ir praktika 7, no. 2 (June 27, 2006): 73–80. http://dx.doi.org/10.3846/btp.2006.09.

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Chiu, Chun-Yuan, Tian-Shyr Dai, Yuh-Dauh Lyuu, Liang-Chih Liu, and Yu-Ting Chen. "Option pricing with the control variate technique beyond Monte Carlo simulation." North American Journal of Economics and Finance 62 (November 2022): 101772. http://dx.doi.org/10.1016/j.najef.2022.101772.

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Kadume, Noor H., and Hafeth I. Naji. "Building Schedule Risks Simulation by Using BIM with Monte Carlo Technique." IOP Conference Series: Earth and Environmental Science 856, no. 1 (September 1, 2021): 012059. http://dx.doi.org/10.1088/1755-1315/856/1/012059.

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Keping, DU, and XUE Kun. "Accelerating Monte Carlo radiative transfer simulation of water using GPU technique." Journal of Lake Sciences 28, no. 3 (2016): 654–60. http://dx.doi.org/10.18307/2016.0322.

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Yamamoto, Toshihiro. "Applicability of non-analog Monte Carlo technique to reactor noise simulation." Annals of Nuclear Energy 38, no. 2-3 (February 2011): 647–55. http://dx.doi.org/10.1016/j.anucene.2010.09.004.

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Kreiner, J. H., and C. S. Putcha. "A computer aided technique for shaft design through monte carlo simulation." Computers & Structures 52, no. 2 (July 1994): 367–72. http://dx.doi.org/10.1016/0045-7949(94)90288-7.

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Lee, Ouk Sub, and Dong Hyeok Kim. "Reliability Estimation of Buried Pipeline Using FORM, SORM and Monte Carlo Simulation." Key Engineering Materials 326-328 (December 2006): 597–600. http://dx.doi.org/10.4028/www.scientific.net/kem.326-328.597.

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In this paper, the failure probability is estimated by using the FORM (first order reliability method), the SORM (second order reliability method) and the Monte Carlo simulation to evaluate the reliability of the corroded pipeline. It is found that the FORM technique is more effective in estimating the failure probability than the SORM technique for B31G and MB31G models with three different corrosion models. Furthermore, it is noted that the difference between the results of the FORM, the SORM and the Monte Carlo simulation decreases with the increase of the exposure time.

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Hassan, M. A., M. B. Elfiky, Y. Nukman, and Reza Mahmoodian. "Monte Carlo Simulation Model for Magnetron Sputtering Deposition." Advanced Materials Research 1105 (May 2015): 69–73. http://dx.doi.org/10.4028/www.scientific.net/amr.1105.69.

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Thin Film layers of metal are often prepared by magnetron sputtering technique for electronic, optical and micro/nanoelectromechanical systems. Usually, experimental work is a common way to find out the optimum deposition conditions and correlate between the thin film properties and the deposition parameters. However, experimental methods are very exhaustive, time and cost-consuming. A good simulation model which can provide the optimum operating conditions to avoid exhaustive experiments and reduce time and cost is highly recommended. Therefore, the present paper is focusing on the development of a computer simulation model of the deposition process in the magnetron sputtering system since such type of models is not well established yet. Monte Carlo (MC) simulation model has been developed to study the effects of deposition parameters on the deposition rate and thin film thickness uniformity. Titanium (Ti) samples were used as the target whereas argon (Ar) was the ambient inert gas. MC simulation has successfully predicted the optimum deposition rate and thickness of Ti thin films on the plastic substrate. The model also depicted the performance of magnetron deposition due to change of processing parameters. Comparison between the simulation and experimental results proved the validity of the proposed model.

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Li, Da Wei, Zhen Zhou Lu, and Zhang Chun Tang. "Local Monte Carlo Simulation for the Reliability Sensitivity Analysis." Advanced Materials Research 291-294 (July 2011): 2183–88. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.2183.

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An efficient numerical technique, namely the Local Monte Carlo Simulation method, is presented to assess the reliability sensitivity in this paper. Firstly some samples are obtained by the random sampling, then the local domain with a constant probability content corresponding to each sample point can be defined, finally the conditional reliability and reliability sensitivity corresponding to every local region can be calculated by using linear approximation of the limit state function. The reliability and reliability sensitivity can be estimated by the expectation of all the conditional reliability and reliability sensitivity. Three examples testify the applicability, validity and accuracy of the proposed method. The results computed by the Local Monte Carlo Simulation method and the Monte Carlo method are compared, which demonstrates that, without losing precision, the computational cost by the former method is much less than the later.

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Newbury, Dale E., and Robert L. Myklebust. "Monte Carlo electron trajectory simulation of x-ray emission from films supported on substrates." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 2 (August 12, 1990): 196–97. http://dx.doi.org/10.1017/s0424820100134570.

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Monte Carlo electron trajectory simulation provides a powerful tool for theoretical studies of electron/x-ray interactions in solid targets. The great power of a Monte Carlo technique arises from the stepwise treatment of the electron trajectory in the target, which provides the capability of dealing with unusual geometric boundary conditions which differ from the ideal of a flat, polished, semiinfinite slab. Special targets, such as randomly tilted surfaces, particles, thin foils, and films supported on substrates, can be simulated by a Monte Carlo procedure.It is proper to speak of “a” rather than “the” Monte Carlo procedure because there is no single formulation of the simulation in universal use. A variety of choices exists for the treatment of the scattering and energy loss processes. In particular, the approach to simulating inelastic scattering varies widely. While most calculations consider inelastic scattering by means of a continuous energy loss approximation rather than by modeling the discrete inelastic processes, there are now several different choices proposed for the treatment of energy loss in the low energy range (<3 keV) where the Bethe model proves inadequate.

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Jakumeit, Jürgen, Amanda Duncan, Umberto Ravaioli, and Karl Hess. "Simulation of Si-MOSFETs with the Mutation Operator Monte Carlo Method." VLSI Design 8, no. 1-4 (January 1, 1998): 343–47. http://dx.doi.org/10.1155/1998/80689.

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The Mutation Operator Monte Carlo method (MOMC) is a new type of Monte Carlo technique for the study of hot electron related effects in semiconductor devices. The MOMC calculates energy distributions of electrons by a physical mutation of the distribution towards a stationary condition. In this work we compare results of an one dimensional simulation of an 800nm Si-MOSFET with full band Monte Carlo calculations and measurement results. Starting from the potential distribution resulting from a drift diffusion simulation, the MOMC calculates electron distributions which are comparable to FBMC-results within minutes on a modern workstation. From these distributions, substrate and gate currents close to experimental results can be calculated. These results show that the MOMC is useful as a post-processor for the investigation of hot electron related problems in Si-MOSFETs. Beside the computational efficiency, a further advantage of the MOMC compared to standard MC techniques is the good resolution of the high energy tail of the distribution without the necessity of any statistical enhancement.

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Marcu, Mihail, and Jürgen Müller. "Variance reduction technique for quantum Monte Carlo simulations." Physics Letters A 119, no. 3 (December 1986): 130–32. http://dx.doi.org/10.1016/0375-9601(86)90430-5.

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