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Journal articles on the topic 'Monte Carlo simulation model'

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Author: Grafiati

Published: 9 March 2023

Last updated: 11 March 2023

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1

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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2

Bravyi, Sergey. "Monte Carlo simulation of stoquastic Hamiltonians." Quantum Information and Computation 15, no. 13&14 (October 2015): 1122–40. http://dx.doi.org/10.26421/qic15.13-14-3.

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Stoquastic Hamiltonians are characterized by the property that their off-diagonal matrix elements in the standard product basis are real and non-positive. Many interesting quantum models fall into this class including the Transverse field Ising Model (TIM), the Heisenberg model on bipartite graphs, and the bosonic Hubbard model. Here we consider the problem of estimating the ground state energy of a local stoquastic Hamiltonian $H$ with a promise that the ground state of $H$ has a non-negligible correlation with some `guiding' state that admits a concise classical description. A formalized version of this problem called Guided Stoquastic Hamiltonian is shown to be complete for the complexity class $\MA$ (a probabilistic analogue of $\NP$). To prove this result we employ the Projection Monte Carlo algorithm with a variable number of walkers. Secondly, we show that the ground state and thermal equilibrium properties of the ferromagnetic TIM can be simulated in polynomial time on a classical probabilistic computer. This result is based on the approximation algorithm for the classical ferromagnetic Ising model due to Jerrum and Sinclair (1993).

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3

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

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

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4

Jin, Wen-Long, and Wilfred W. Recker. "Monte Carlo Simulation Model of Intervehicle Communication." Transportation Research Record: Journal of the Transportation Research Board 2000, no. 1 (January 2007): 8–15. http://dx.doi.org/10.3141/2000-02.

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5

Panov, Yu D., A. S. Moskvin, A. A. Chikov, and V. A. Ulitko. "Monte Carlo simulation of a model cuprate." Journal of Physics: Conference Series 2043, no. 1 (October 1, 2021): 012007. http://dx.doi.org/10.1088/1742-6596/2043/1/012007.

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6

Zhang, Ji-xiang, Hui Wen, and Yun-teng Liu. "Monte carlo model in metal recrystallization simulation." Journal of Shanghai Jiaotong University (Science) 16, no. 3 (June 2011): 337–42. http://dx.doi.org/10.1007/s12204-011-1156-x.

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7

Saly, Rudolf. "Monte Carlo simulation of lattice Skyrme model." Computer Physics Communications 36, no. 4 (June 1985): 417–22. http://dx.doi.org/10.1016/0010-4655(85)90031-1.

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8

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

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

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9

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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10

HATANO, NAOMICHI. "MONTE CARLO SIMULATION OF RANDOM BOSON HUBBARD MODEL." International Journal of Modern Physics C 07, no. 03 (June 1996): 449–56. http://dx.doi.org/10.1142/s0129183196000405.

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A Monte Carlo algorithm for the random Boson Hubbard model is reported. The analytic expression of the matrix elements is presented, and the ergodicity of the Monte Carlo flips is discussed. The results in one dimension supports a previously proposed perturbational scaling argument.

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11

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

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

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12

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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13

PARAMARTHA, I. PUTU OKA, KOMANG DHARMAWAN, and DESAK PUTU EKA NILAKUSMAWATI. "PENENTUAN HARGA KONTRAK OPSI TIPE ASIA MENGGUNAKAN MODEL SIMULASI NORMAL INVERSE GAUSSIAN (NIG)." E-Jurnal Matematika 3, no. 3 (August 29, 2014): 123. http://dx.doi.org/10.24843/mtk.2014.v03.i03.p074.

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The aim to determine of the simulation results and to calculate the stock price of Asian Option with Normal Inverse Gaussian (NIG) method and Monte Carlo method using MATLAB program. Results of both models are compared and selected a fair price. Besides to determine simulation accuracy of the stock price, speed of program execution MATLAB is calculated for both models for time efficiency. The first part, set variabels used to calculate the trajectory of stock prices at time t to simulate the stock price at the time. The second part, simulate the stock price with NIG model. The third part, simulate the stock price with Monte Carlo model. After simulating the stock price, calculated the value of the pay-off of the Asian Option, and then estimate the price of Asian Option by averaging the entire value of pay-off from each iteration. The last part, compare result of both models. The results of this research is price of Asian Option calculated using Monte Carlo simulation and NIG. The rates were calculated using the NIG produce a fair price, because of the pricing contract NIG using four parameters ?, ?, ?, and ?, while Monte Carlo is using only two parameters ? and ?. For execution time of the program, the Monte Carlo model is better in all iterations.

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14

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

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

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15

Hostikka, S., T. Korhonen, and O. Keski-Rahkonen. "Two-model Monte Carlo Simulation Of Fire Scenarios." Fire Safety Science 8 (2005): 1241–52. http://dx.doi.org/10.3801/iafss.fss.8-1241.

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16

Song, Cui Ying, and Chuan Dong Li. "Simulation of Ising Model by Monte Carlo Method." Advanced Materials Research 936 (June 2014): 2271–75. http://dx.doi.org/10.4028/www.scientific.net/amr.936.2271.

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Simulating Ising model to calculate magnetization intensity by Monte Carlo method. The Ising model was introduced simply, sampled importantly, and calculated with programming. It shows the dependency relationship between the magnetization intensity and the size of dot-square line in different temperatures for Ising model. It cans edulcorate the approximation of analytic method by computer simulating. It obtains a method to appraise a model right or wrong by comparing the model and the experimental data.

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17

Wang, Xidi, David W. Brown, and Katja Lindenberg. "Quantum Monte Carlo Simulation of the Davydov Model." Physical Review Letters 63, no. 5 (July 31, 1989): 584. http://dx.doi.org/10.1103/physrevlett.63.584.

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18

Elidrysy, A., S. Harir, A. Zouhair, and Y. Boughaleb. "3D anisotropic Ising model with Monte Carlo simulation." IOP Conference Series: Materials Science and Engineering 948 (November 14, 2020): 012001. http://dx.doi.org/10.1088/1757-899x/948/1/012001.

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19

Akai Kurbanovich, Murtazaev, and Ibaev Zhavrail Gadzhievich. "The Monte Carlo simulation of 2D ANNNI-model." EPJ Web of Conferences 185 (2018): 11010. http://dx.doi.org/10.1051/epjconf/201818511010.

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In this, study we present the data for 2D Axial Next Nearest Neighbor Ising model (ANNNI-model) obtained from Monte Carlo (MC) simulations using the standard Metropolis algorithm. The temperature dependences of thermodynamic parameters for a cubic lattice with linear sizes L=32 at different values of the competing interaction parameter |J1/J|=0.1÷1.0. Transition temperatures of ferromagnetic ordering to the paramagnetic state at |J1/J|<0.3 and to the modulated state at 0.3<|J1/J|<0.5 are shown to shift towards low temperatures with an increase in a competing interaction parameter absolute value. Conversely, transition temperatures of the modulate state to the paramagnetic ordering grow. The modulated ordering in the 2D ANNNImodel appears in the temperature range 0.1<T<2.0 at 0.2<|J1/J|≤1.0. Modulated structure parameters are computed using a mathematic apparatus of Fourier transform spectral analysis. According to the Fourier analysis results, the wave number grows with an increase in the competing interaction parameter absolute value. Summarizing obtained results, we plot a phase diagram of 2D anisotropic Ising model with competing interactions.

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20

Whitmire, David, Larry Cornelius, and Paula Whitmire. "Monte Carlo Simulation of an Ethanol Pharmacokinetic Model." Alcoholism: Clinical & Experimental Research 26, no. 10 (October 2002): 1484–93. http://dx.doi.org/10.1097/00000374-200210000-00005.

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21

Whitmire, David, Larry Cornelius, and Paula Whitmire. "Monte Carlo Simulation of an Ethanol Pharmacokinetic Model." Alcoholism: Clinical and Experimental Research 26, no. 10 (October 2002): 1484–93. http://dx.doi.org/10.1111/j.1530-0277.2002.tb02447.x.

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22

Wang, Xidi, David W. Brown, and Katja Lindenberg. "Quantum Monte Carlo simulation of the Davydov model." Physical Review Letters 62, no. 15 (April 10, 1989): 1796–99. http://dx.doi.org/10.1103/physrevlett.62.1796.

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23

Hughes, R. E., and K. N. An. "Monte Carlo simulation of a planar shoulder model." Medical & Biological Engineering & Computing 35, no. 5 (September 1997): 544–48. http://dx.doi.org/10.1007/bf02525538.

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24

Faas, M., and H. J. Hilhorst. "Hierarchical Monte Carlo simulation of the Ising model." Physica A: Statistical Mechanics and its Applications 135, no. 2-3 (April 1986): 571–90. http://dx.doi.org/10.1016/0378-4371(86)90161-5.

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25

Huang, Zhao Dong, Wen Bing Chang, Yi Yong Xiao, and Rui Liu. "An Extended Monte Carlo Method on Simulating the Development Cost Uncertainties of Aircraft." Advanced Materials Research 118-120 (June 2010): 810–14. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.810.

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Monte Carlo Simulation is a general method for evaluating a deterministic model by iteratively generating inputs so as to get the natural distribution of outputs, which has often been employed for risk analysis of development cost estimation under uncertain environment. However, the traditional way of implementing Monte Carlo Simulation on cost risk analysis is always based on deterministic Cost Estimation Relation (CER) model and does not take the uncertainty of history cost data used to build CER into account, which will considerably affect the cost risk analysis. In this paper, we extend Monte Carlo Simulation model to make its simulating process cover the stage of building model so that not only the inputs are iteratively generated but also the model is iteratively rebuilt. An example is carried out to compare the extended model to the traditional one on analyzing aircraft development cost risk, which shows that the risk distribution gotten by Extended Monte Carlo Simulation is considerably different to that gotten by traditional one.

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26

Feng, Mingbin, and Jeremy Staum. "Green Simulation with Database Monte Carlo." ACM Transactions on Modeling and Computer Simulation 31, no. 1 (February 2021): 1–26. http://dx.doi.org/10.1145/3429336.

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In a setting in which experiments are performed repeatedly with the same simulation model, green simulation means reusing outputs from previous experiments to answer the question currently being asked of the model. In this article, we address the setting in which experiments are run to answer questions quickly, with a time limit providing a fixed computational budget, and then idle time is available for further experimentation before the next question is asked. The general strategy is database Monte Carlo for green simulation: the output of experiments is stored in a database and used to improve the computational efficiency of future experiments. In this article, the database provides a quasi-control variate, which reduces the variance of the estimated mean response in a future experiment that has a fixed computational budget. We propose a particular green simulation procedure using quasi-control variates, addressing practical issues such as experiment design, and analyze its theoretical properties. We show that, under some conditions, the variance of the estimated mean response in an experiment with a fixed computational budget drops to zero over a sequence of repeated experiments, as more and more idle time is invested in creating databases. Our numerical experiments on the procedure show that using idle time to create databases of simulation output provides variance reduction immediately, and that the variance reduction grows over time in a way that is consistent with the convergence analysis.

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27

Stamhuis, J. E., W. G. Heitman, H. van Ballegooijen, and D. J. Osborne. "Monte Carlo Simulation of Styrene-Butadiene Elastomers." Rubber Chemistry and Technology 61, no. 5 (November 1, 1988): 783–93. http://dx.doi.org/10.5254/1.3536218.

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Abstract A relatively simple model for SBR networks has been developed that is based on reaction kinetics. In addition, any copolymer structure not directly originating from reaction kinetics but from the polymer physicist's imagination can be fed into the model; the only thing to be done in this case is to construct an imaginary polymer composition curve as input for the model. The model then visualizes in a quantitative manner the resulting polymer chains and their structure in the network in terms of monomer sequence distribution, network segment distribution, and the distribution of the crosslinks over the individual chains. These features are essentially the basis of further work regarding the mechanical and viscoelastic properties and, ultimately, the road behavior of solution-SBR-based tread vulcanizates.

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BALDEAUX, JAN. "EXACT SIMULATION OF THE 3/2 MODEL." International Journal of Theoretical and Applied Finance 15, no. 05 (August 2012): 1250032. http://dx.doi.org/10.1142/s021902491250032x.

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This paper discusses the exact simulation of the stock price process underlying the 3/2 model. Using a result derived by Craddock and Lennox using Lie Symmetry Analysis, we adapt the Broadie-Kaya algorithm for the simulation of affine processes to the 3/2 model. We also discuss variance reduction techniques and find that conditional Monte Carlo techniques combined with quasi-Monte Carlo point sets result in significant variance reductions.

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29

Mehadji, Brahim, Mathieu Dupont, Denis Fougeron, and Christian Morel. "Monte Carlo simulation of SiPMs with GATE." Journal of Instrumentation 17, no. 09 (September 1, 2022): P09025. http://dx.doi.org/10.1088/1748-0221/17/09/p09025.

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Abstract Silicon photomultipliers (SiPMs) replace photomultiplier tubes (PMTs) for the detection of light in many applications, particularly in high energy physics and medical imaging. We describe a flexible implementation of a SiPM model for the GATE Monte Carlo simulation platform, which is based on the SiPM noise description proposed by Rosado and Hidalgo, and describe an easy and effective method to determine and instantiate the SiPM noise model with simple measurements. We also simulate the micro-cell Single Photon Time Resolution (SPTR) and describe its measurement.

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Barkema, G. T., and M. E. J. Newman. "Monte Carlo simulation of ice models." Physical Review E 57, no. 1 (January 1, 1998): 1155–66. http://dx.doi.org/10.1103/physreve.57.1155.

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31

Liu, Yang. "M/M/1 Queue Model with Uncertain Parameters by Monte Carlo Simulation." Applied Mechanics and Materials 556-562 (May 2014): 3541–44. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3541.

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M/M/1 model was the most important and basic of all queue models. The paper combined Monte Carlo simulation with traditional M/M/1 model. The paper set an example of traditional M/M/1 system and calculates its indicators of performance firstly. Secondly, define the parameters with combination of the observation data and Delphi results. Thirdly, do Monte Carlo simulation with computer and software. In the end, compare the results by two methods. The results showed that the performance could be acceptable and did not need to make any improvements in case of traditional calculation. But, lots of things had been changed when the parameters had become uncertain. All indicators showed a big risk after the Monte Carlo simulation. Compare the results of traditional M/M/1 model and Monte Carlo simulation; it was found that it was necessary to treat this kind of problem as an uncertain problem in order to improve the accuracy of decision making.

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Pakyuz-Charrier, Evren, Mark Jessell, Jérémie Giraud, Mark Lindsay, and Vitaliy Ogarko. "Topological analysis in Monte Carlo simulation for uncertainty propagation." Solid Earth 10, no. 5 (October 10, 2019): 1663–84. http://dx.doi.org/10.5194/se-10-1663-2019.

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Abstract. This paper proposes and demonstrates improvements for the Monte Carlo simulation for uncertainty propagation (MCUP) method. MCUP is a type of Bayesian Monte Carlo method aimed at input data uncertainty propagation in implicit 3-D geological modeling. In the Monte Carlo process, a series of statistically plausible models is built from the input dataset of which uncertainty is to be propagated to a final probabilistic geological model or uncertainty index model. Significant differences in terms of topology are observed in the plausible model suite that is generated as an intermediary step in MCUP. These differences are interpreted as analogous to population heterogeneity. The source of this heterogeneity is traced to be the non-linear relationship between plausible datasets' variability and plausible model's variability. Non-linearity is shown to mainly arise from the effect of the geometrical rule set on model building which transforms lithological continuous interfaces into discontinuous piecewise ones. Plausible model heterogeneity induces topological heterogeneity and challenges the underlying assumption of homogeneity which global uncertainty estimates rely on. To address this issue, a method for topological analysis applied to the plausible model suite in MCUP is introduced. Boolean topological signatures recording lithological unit adjacency are used as n-dimensional points to be considered individually or clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The proposed method is tested on two challenging synthetic examples with varying levels of confidence in the structural input data. Results indicate that topological signatures constitute a powerful discriminant to address plausible model heterogeneity. Basic topological signatures appear to be a reliable indicator of the structural behavior of the plausible models and provide useful geological insights. Moreover, ignoring heterogeneity was found to be detrimental to the accuracy and relevance of the probabilistic geological models and uncertainty index models. Highlights. Monte Carlo uncertainty propagation (MCUP) methods often produce topologically distinct plausible models. Plausible models can be differentiated using topological signatures. Topologically similar probabilistic geological models may be obtained through topological signature clustering.

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ZHANG, X. Y. "QUANTUM MONTE CARLO ALGORITHM FOR CONSTRAINED FERMIONS." Modern Physics Letters B 05, no. 19 (August 20, 1991): 1255–65. http://dx.doi.org/10.1142/s0217984991001532.

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Using path integral quantization in the subspace that forbids double occupancy, we introduce a quantum Monte Carlo algorithm for simulation of fermion models with constraint. The algorithm can be applied to a class of lattice fermion models, including the infinite-U Hubbard model and the t - J model.

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GEPNER, DORON. "MONTE–CARLO THERMODYNAMIC BETHE ANSATZ." International Journal of Modern Physics B 20, no. 14 (June 10, 2006): 2049–64. http://dx.doi.org/10.1142/s0217979206034522.

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We introduce a Monte–Carlo simulation approach to thermodynamic Bethe ansatz (TBA). We exemplify the method on one-particle integrable models, which include a free boson and a free fermions systems along with the scaling Lee–Yang model (SLYM). It is confirmed that the central charges and energies are correct to a very good precision, typically 0.1% or so. The advantage of the method is that it allows the calculation of all the dimensions and even the particular partition function.

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35

PENNA, T. J. P., and D. STAUFFER. "EFFICIENT MONTE CARLO SIMULATION OF BIOLOGICAL AGING." International Journal of Modern Physics C 06, no. 02 (April 1995): 233–39. http://dx.doi.org/10.1142/s0129183195000186.

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A bit-string model of biological life-histories is parallelized, with hundreds of millions of individuals. It gives the desired drastic decay of survival probabilities with increasing age for 32 age intervals.

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Wartono, Wartono, Dwi Hartoyo, Nilasari Nilasari, and John Rafafy Batlolona. "Real-virtual Monte Carlo simulation on impulse-momentum and collisions." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 1 (January 1, 2019): 7. http://dx.doi.org/10.11591/ijeecs.v13.i1.pp7-14.

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<p>This study aims to determine the differences in scientific literacy of students who were given inquiry learning through a real-virtual Monte Carlo experiment with students who were given conventional learning. This study used quasi experimental design with pretest-posttest control group. The results showed that the ability of students who were taught by inquiry learning models through real-virtual Monte Carlo experiments had higher scientific literacy than those taught with conventional models, it also applied well to students with high and low initial abilities. The results of the average gain in scientific literacy scores also showed a higher value between students who studied with the inquiry model through a real-virtual Monte Carlo experiment with students who studied with conventional models. The novelty of this research is combining real and virtual activities become real-virtual Monte Carlo by using the inquiry learning model to improve students' scientific literacy.</p>

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Maruyama, Ken-ichi, and Yasushi Fujiyoshi. "Monte Carlo Simulation of the Formation of Snowflakes." Journal of the Atmospheric Sciences 62, no. 5 (May 1, 2005): 1529–44. http://dx.doi.org/10.1175/jas3416.1.

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Abstract A stochastic microphysical model of snow aggregation that combines a simple aggregation model with a Monte Carlo method was developed. Explicit treatment of the shape of individual snowflakes in the new model facilitates examination of the structure of snowflakes and the relationships between the parameters of the generated snowflakes, such as mass versus diameter, in addition to comparisons with observations. In this study, complexities in the shape of snowflakes are successfully simulated, and the understanding of the evolution of their size distribution is advanced. The mean diameter of snow particles evolves more rapidly in the aggregate model than in the sphere model. However, growth rates of the aggregates greatly depend on the collision section of particles in aggregation. The mean mass of snowflakes in the aggregate model grows more slowly than the mass in the sphere model when the sum of the particle cross section is used as the collision cross section. The mean mass grows more quickly when a circle is used whose radius is the sum of the radii of two particles. Sensitivity experiments showed that aggregation also depends on the mean and standard deviation of the initial distribution, and on the density of constituent particles.

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Wang Xiang, 王翔, 裴香涛 Pei Xiangtao, 邵鹏 Shao Peng, and 黄文浩 Huang Wenhao. "Monte Carlo Simulation Model of Micro Light Beam Solidification." Acta Optica Sinica 28, no. 12 (2008): 2388–93. http://dx.doi.org/10.3788/aos20082812.2388.

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Méndez-Giono, J. A., T. Minea, T. Thuillier, and A. Revel. "Self-consistent Monte Carlo model for ECRIS plasma simulation." Journal of Physics: Conference Series 2244, no. 1 (April 1, 2022): 012027. http://dx.doi.org/10.1088/1742-6596/2244/1/012027.

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Abstract A self-consistent iterative Monte Carlo model to simulate electron cyclotron resonance ion source (ECRIS) plasma is presented. It computes the species’ spatial and energy distribution in the whole plasma chamber in a three-dimensional mesh. A number of electrons and ions are propagated independently considering the static magnetic field, injected microwave field and local electrical potential field. The species trajectories populate the mesh allowing to compute their local density and velocity. Each species is pushed until it undergoes a destructive collision or after a fixed time limit. After each propagation phase, the local plasma potential and the heating electromagnetic microwave field are updated. This process is then iterated until convergence of species distributions and fields is reached. This method is intended to be a faster alternative to other methods to characterise the species distributions in the plasma for a specified ECRIS design and aid with their conception. The model and software development status are presented, along with prospects.

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Kobayashi, Masakazu, and Yoshimasa Takayama. "Monte Carlo simulation of microstructural evolution using Potts model." Journal of Japan Institute of Light Metals 54, no. 4 (April 30, 2004): 159–65. http://dx.doi.org/10.2464/jilm.54.159.

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Muhamad Safiih, L. "Fuzzy parametric sample selection model: Monte Carlo simulation approach." Journal of Statistical Computation and Simulation 83, no. 6 (June 2013): 992–1006. http://dx.doi.org/10.1080/00949655.2011.646277.

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Miura, H. "Crystal structure model assembly program using Monte Carlo simulation." Acta Crystallographica Section A Foundations of Crystallography 64, a1 (August 23, 2008): C212. http://dx.doi.org/10.1107/s0108767308093197.

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Kurniadi, Rizal, Abdul Waris, and Sparisoma Viridi. "Monte Carlo simulation based toy model for fission process." International Journal of Modern Physics C 27, no. 03 (February 23, 2016): 1650030. http://dx.doi.org/10.1142/s0129183116500303.

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Nuclear fission has been modeled notoriously using two approaches method, macroscopic and microscopic. This work will propose another approach, where the nucleus is treated as a toy model. The aim is to see the usefulness of particle distribution in fission yield calculation. Inasmuch nucleus is a toy, then the Fission Toy Model (FTM) does not represent real process in nature completely. The fission event in FTM is represented by one random number. The number is assumed as width of distribution probability of nucleon position in compound nuclei when fission process is started. By adopting the nucleon density approximation, the Gaussian distribution is chosen as particle distribution. This distribution function generates random number that randomizes distance between particles and a central point. The scission process is started by smashing compound nucleus central point into two parts that are left central and right central points. The yield is determined from portion of nuclei distribution which is proportional with portion of mass numbers. By using modified FTM, characteristic of particle distribution in each fission event could be formed before fission process. These characteristics could be used to make prediction about real nucleons interaction in fission process. The results of FTM calculation give information that the [Formula: see text] value seems as energy.

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López, H., X. Oriols, J. Suñé, and X. Cartoixà. "Spin-dependent injection model for Monte Carlo device simulation." Journal of Applied Physics 104, no. 7 (2008): 073702. http://dx.doi.org/10.1063/1.2986137.

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Kamakura, Y., H. Mizuno, M. Yamaji, M. Morifuji, K. Taniguchi, C. Hamaguchi, T. Kunikiyo, and M. Takenaka. "Impact ionization model for full band Monte Carlo simulation." Journal of Applied Physics 75, no. 7 (April 1994): 3500–3506. http://dx.doi.org/10.1063/1.356112.

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Nicolaides, D. B. "Monte Carlo simulation of the fully frustrated XY model." Journal of Physics A: Mathematical and General 24, no. 5 (March 7, 1991): L231—L235. http://dx.doi.org/10.1088/0305-4470/24/5/004.

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de Oliveira, M. J., and J. R. N. Chiappin. "Monte Carlo simulation of the quantum transverse Ising model." Physica A: Statistical Mechanics and its Applications 238, no. 1-4 (April 1997): 307–16. http://dx.doi.org/10.1016/s0378-4371(96)00461-x.

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Luque, J. J., A. Gómez, and A. Córdoba. "CO+NO surface reaction model by Monte Carlo simulation." Physica A: Statistical Mechanics and its Applications 331, no. 3-4 (January 2004): 505–16. http://dx.doi.org/10.1016/j.physa.2003.01.002.

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Hassan, H. A., and David B. Hash. "A generalized hard‐sphere model for Monte Carlo simulation." Physics of Fluids A: Fluid Dynamics 5, no. 3 (March 1993): 738–44. http://dx.doi.org/10.1063/1.858656.

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Del Debbio, Luigi, and Simon Hands. "Monte Carlo simulation of the three dimensional Thirring model." Physics Letters B 373, no. 1-3 (April 1996): 171–77. http://dx.doi.org/10.1016/0370-2693(96)00137-2.

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