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Relevant bibliographies by topics / Monte Carlo Simulation Technique

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Academic literature on the topic 'Monte Carlo Simulation Technique'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Monte Carlo Simulation Technique"

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JAKUMEIT, JÜRGEN. "COMPUTATIONAL ASPECTS OF THE LOCAL ITERATIVE MONTE CARLO TECHNIQUE." International Journal of Modern Physics C 11, no. 04 (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 simu

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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 (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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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

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Huang, Xiangyuan. "Consumer and Marketing Research Using the Monte Carlo Simulation." Advances in Economics, Management and Political Sciences 32, no. 1 (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

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

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

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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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Chen, Jiming. "Monte Carlo Simulations in Complex Systems: Challenges and New Approaches." Theoretical and Natural Science 86, no. 1 (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

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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 (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 use

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Mo, Zihan, Boxu He, and Tian Qin. "Option Pricing Based on Several Monte Carlo Techniques." Theoretical and Natural Science 107, no. 1 (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

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Tamošiūnienė, Rima. "The Use of Monte Carlo Simulation Technique to Support Investment Decisions." Business: Theory and Practice 7, no. (2) (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 a

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Dissertations / Theses on the topic "Monte Carlo Simulation Technique"

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Rangaraj, Dharanipathy. "Multicomponent aerosol dynamics : exploration of direct simulation Monte Carlo technique /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144452.

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Jackson, Andrew N. "Structural phase behaviour via Monte Carlo techniques." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/4850.

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There are few reliable computational techniques applicable to the problem of structural phase behaviour. This is starkly emphasised by the fact that there are still a number of unanswered questions concerning the solid state of some of the simplest models of matter. To determine the phase behaviour of a given system we invoke the machinery of statistical physics, which identifies the equilibrium phase as that which minimises the free-energy. This type of problem can only be dealt with fully via numerical simulation, as any less direct approach will involve making some uncontrolled approximatio

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Can, Mutan Oya. "Comparison Of Regression Techniques Via Monte Carlo Simulation." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605175/index.pdf.

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The ordinary least squares (OLS) is one of the most widely used methods for modelling the functional relationship between variables. However, this estimation procedure counts on some assumptions and the violation of these assumptions may lead to nonrobust estimates. In this study, the simple linear regression model is investigated for conditions in which the distribution of the error terms is Generalised Logistic. Some robust and nonparametric methods such as modified maximum likelihood (MML), least absolute deviations (LAD), Winsorized least squares, least trimmed squares (LTS), Theil and wei

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Louvin, Henri. "Development of an adaptive variance reduction technique for Monte Carlo particle transport." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS351/document.

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L’algorithme Adaptive Multilevel Splitting (AMS) a récemment fait son apparition dans la littérature de mathématiques appliquées, en tant que méthode de réduction de variance pour la simulation Monte Carlo de chaı̂nes de Markov. Ce travail de thèse se propose d’implémenter cette méthode de réduction de variance adaptative dans le code Monte-Carlo de transport de particules TRIPOLI-4,dédié entre autres aux études de radioprotection et d’instrumentation nucléaire. Caractérisées par de fortes atténuations des rayonnements dans la matière, ces études entrent dans la problématique

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Nilsson, Emma. "Monte Carlo simulation techniques : The development of a general framework." Thesis, Linköping University, Department of Management and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-18327.

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<p>Algorithmica Research AB develops software application for the financial markets. One of their products is Quantlab that is a tool for quantitative analyses. An effective method to value several financial instruments is Monte Carlo simulation. Since it is a common method Algorithmica is interesting in investigating if it is possible to create a Monte Carlo framework.</p><p>A requirement from Algorithmica is that the framework is general and this is the main problem to solve. It is difficult to generate a generalized framework because financial derivatives have very different appearances. To

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FOIS, GIOVANNA ROSA. "Monte Carlo simulation studies for spatially fractionated radiation therapy techniques." Doctoral thesis, Università degli Studi di Cagliari, 2013. http://hdl.handle.net/11584/266224.

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The purpose of this work was to evaluate the possible gain in tissue sparing in hadrontherapy when a spatial fractionation of the dose is used. Monte Carlo simulations (GATE v.6) have been used as a method to assess the dose distributions in a cylindrical water phantom (16 cm height and 16 cm of diameter). The phantom was irradiated with carbon and oxygen minibeams of 0.7 mm of beam width, and with protons in a grid geometry configuration. Several centre-to-centre distances, covering a 2 x 2 cm2 area, have been taken into account. The ratio of the peak-to-valley doses (PVDR) and the penumbrae

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Ahmad, Abdul Ossman. "Advances in an open-source direct simulation Monte Carlo technique for hypersonic rarefied gas flows." Thesis, University of Strathclyde, 2013. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=26579.

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Hypersonic vehicles that travel through rarefied gas environments are very expensive to design through experimental methods. In the last few decades major work has been carried out in developing numerical methods to capture these types of flows to a certain degree of accuracy. This accuracy is increased using particle based numerical techniques as opposed to continuum computational fluid dynamics. However, one of the modern problems of particle based techniques is the high computational cost associated with it. This thesis presents an enhanced open-source particle based technique to capture hi

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Lester, Christopher. "Efficient simulation techniques for biochemical reaction networks." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:bb804e01-b1de-409f-b843-4806c2c990c2.

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Discrete-state, continuous-time Markov models are becoming commonplace in the modelling of biochemical processes. The mathematical formulations that such models lead to are opaque, and, due to their complexity, are often considered analytically intractable. As such, a variety of Monte Carlo simulation algorithms have been developed to explore model dynamics empirically. Whilst well-known methods, such as the Gillespie Algorithm, can be implemented to investigate a given model, the computational demands of traditional simulation techniques remain a significant barrier to modern research. In ord

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Baker, Adam Richard Ernest. "The use of the Monte Carlo technique in the simulation of small-scale dosimeters and microdosimeters." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/2897/.

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In order to understand the effects of low keV radiation upon small scales, a number of detector designs have been developed to investigate the ways energy is deposited. This research was conducted in order to investigate a number of different detector designs, looking in particular at their properties as small scale dosimeters exposed to photon radiation with an energy of 5-50 keV. In addition to this, Monte Carlo models were constructed of the different detector designs in order to ascertain the trends in energy absorption within the detectors. An important part of the research was investigat

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Mastail, Cédric. "Modélisation et simulation du dépôt des oxydes à forte permittivité par la technique du Monte-Carlo cinétique." Toulouse 3, 2009. http://thesesups.ups-tlse.fr/989/.

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Miniaturiser les composants impose des changements radicaux pour l'élaboration des dispositifs micro électroniques du futur. Dans ce cadre, les oxydes de grille MOS atteignent des épaisseurs limites qui les rendent perméables aux courants de fuite. Une solution est de remplacer le SiO2 par un matériau de permittivité plus élevée permettant l'utilisation de couches plus épaisses pour des performances comparables. Dans ce travail nous présentons une modélisation multi-échelle de la croissance par couche atomique (ALD) d'HfO2 sur Si permettant de relier la nano-structuration d'une interface au pr

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Books on the topic "Monte Carlo Simulation Technique"

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Mun, Johnathan. Modeling risk: Applying Monte Carlo simulation, real options analysis, forecasting, and optimization techniques. 2nd ed. Wiley, 2010.

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A, Viterna Larry, and Lewis Research Center, eds. ETARA PC version 3.3 user's guide: Reliability, availability, maintainability simulation model. NASA Lewis Research Center, 1991.

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A, Viterna Larry, and Lewis Research Center, eds. ETARA PC version 3.3 user's guide: Reliability, availability, maintainability simulation model. NASA Lewis Research Center, 1991.

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A, Viterna Larry, and Lewis Research Center, eds. ETARA PC version 3.3 user's guide: Reliability, availability, maintainability simulation model. NASA Lewis Research Center, 1991.

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Mooney, Christopher. Monte Carlo Simulation. SAGE Publications, Inc., 1997. http://dx.doi.org/10.4135/9781412985116.

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Zhu, Zhen, and Hari Rajagopalan. Monte Carlo Simulation. SAGE Publications, Inc., 2023. http://dx.doi.org/10.4135/9781071908969.

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I, Schueller G., ed. Monte Carlo simulation. A.A. Balkema, 2001.

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Hess, Karl, ed. Monte Carlo Device Simulation. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7.

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Thomopoulos, Nick T. Essentials of Monte Carlo Simulation. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6022-0.

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Brandimarte, Paolo. Handbook in Monte Carlo Simulation. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118593264.

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Book chapters on the topic "Monte Carlo Simulation Technique"

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Frenkel, D. "Advanced Monte Carlo Techniques." In Computer Simulation in Chemical Physics. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1679-4_4.

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Woolard, D. L., H. Tian, M. A. Littlejohn, R. J. Trew, and K. W. Kim. "The Application of Monte Carlo Techniques in Advanced Hydrodynamic Transport Models." In Monte Carlo Device Simulation. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7_8.

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D'Elia, Massimo, Kurt Langfeld, and Biagio Lucini. "Advanced Monte Carlo simulation techniques." In Stochastic Methods in Scientific Computing. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781315156156-5.

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Demirtas, Hakan. "A Multiple Imputation Framework for Massive Multivariate Data of Different Variable Types: A Monte-Carlo Technique." In Monte-Carlo Simulation-Based Statistical Modeling. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3307-0_8.

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Sarrut, D., and N. Krah. "Artificial Intelligence and Monte Carlo Simulation." In Monte Carlo Techniques in Radiation Therapy, 2nd ed. CRC Press, 2021. http://dx.doi.org/10.1201/9781003211846-20.

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Hanagaki, Kazunori, Junichi Tanaka, Makoto Tomoto, and Yuji Yamazaki. "Event Simulation." In Experimental Techniques in Modern High-Energy Physics. Springer Japan, 2022. http://dx.doi.org/10.1007/978-4-431-56931-2_7.

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AbstractNot only real experimental data but also simulated data are necessary for modern data analyses because experiments, that is, detectors, etc. are getting complex so that we need the help of Monte Carlo (MC) simulation to understand the experimental data.

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L'Ecuyer, Pierre, François Le Gland, Pascal Lezaud, and Bruno Tuffin. "Splitting Techniques." In Rare Event Simulation using Monte Carlo Methods. John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745403.ch3.

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Shigeta, K., K. Tanaka, T. Iizuka, H. Kato, and H. Matsumoto. "A New Statistical Enhancement Technique in Parallelized Monte Carlo Device Simulation." In Simulation of Semiconductor Devices and Processes. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-6619-2_93.

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Thomson, Rowan M., Åsa Carlsson Tedgren, Gabriel Fonseca, et al. "Monte Carlo Simulation in Brachytherapy Patients and Applicator Modelling." In Monte Carlo Techniques in Radiation Therapy, 2nd ed. CRC Press, 2021. http://dx.doi.org/10.1201/9781003211846-19.

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Allen, Theodore T. "Variance Reduction Techniques and Quasi-Monte Carlo." In Introduction to Discrete Event Simulation and Agent-based Modeling. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-139-4_8.

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Conference papers on the topic "Monte Carlo Simulation Technique"

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Graham, G. E. "DZero Monte Carlo simulation." In ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH: VII International Workshop; ACAT 2000. AIP, 2001. http://dx.doi.org/10.1063/1.1405336.

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Jain, Pranav, Ziyin Qu, Peter Yichen Chen, and Oded Stein. "Neural Monte Carlo Fluid Simulation." In SIGGRAPH '24: Special Interest Group on Computer Graphics and Interactive Techniques Conference. ACM, 2024. http://dx.doi.org/10.1145/3641519.3657438.

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Baoxin Li, R. Chellappa, and Hankyu Moon. "Monte Carlo simulation techniques for probabilistic tracking." In Conference Record. Thirty-Fifth Asilomar Conference on Signals, Systems and Computers. IEEE, 2001. http://dx.doi.org/10.1109/acssc.2001.986883.

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Amini, Abolfazl M., George E. Ioup, and Juliette W. Ioup. "Metropolis Monte Carlo deconvolution technique compared to iterative methods for noisy data." In Aerospace/Defense Sensing, Simulation, and Controls, edited by Ivan Kadar. SPIE, 2001. http://dx.doi.org/10.1117/12.436983.

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Abu Husain, M. K., N. I. Mohd Zaki, M. B. Johari, and G. Najafian. "Extreme Response Prediction for Fixed Offshore Structures by Monte Carlo Time Simulation Technique." In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54200.

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For an offshore structure, wind, wave, current, tide, ice and gravitational forces are all important sources of loading which exhibit a high degree of statistical uncertainty. The capability to predict the probability distribution of the response extreme values during the service life of the structure is essential for safe and economical design of these structures. Many different techniques have been introduced for evaluation of statistical properties of response. In each case, sea-states are characterised by an appropriate water surface elevation spectrum, covering a wide range of frequencies

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Drosg, M., Marianne E. Hamm, and Robert W. Hamm. "Complete Monte Carlo Simulation of Neutron Scattering Experiments." In APPLICATIONS OF NUCLEAR TECHNIQUES: Eleventh International Conference. AIP, 2011. http://dx.doi.org/10.1063/1.3665300.

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BOWLES, ROLAND, TONY LAITURI, and GEORGE TREVINO. "A Monte Carlo simulation technique for low-altitude, wind-shear turbulence." In 28th Aerospace Sciences Meeting. American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-564.

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Sadowsky, J. S., and J. A. Bucklew. "Large deviations theory techniques in Monte Carlo simulation." In the 21st conference. ACM Press, 1989. http://dx.doi.org/10.1145/76738.76804.

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Perry, Vincent, Wendy Gao, Michael Chen, J. Michael Barton, and Simon Su. "Visualization Techniques for Large-Scale Monte Carlo Simulation." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006423.

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Irimia, Daniel, and Jens O. M. Karlsson. "Monte Carlo Simulation of Ice Formation in Tissues." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-32681.

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Freezing is a common technique for preservation of isolated cells, and extending its applications to the preservation of tissues would have important implications for the storage and distribution of tissue engineered products. Unlike isolated cells in suspension, cells in tissue interact with each other, and this interaction is known to affect the outcome of tissue cryopreservation. As a consequence, our knowledge of the cryobiology of isolated cells cannot simply be extrapolated to tissues, and new models, which consider the interaction between cells, need to be developed. The model that we p

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Reports on the topic "Monte Carlo Simulation Technique"

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Schipaunboord, W. N., M. A. Lont, and A. H. M. Kron. JTM-00-01 NDE Acceptance Criteria for Girth Defects Linked with Welding and Inspection Technique. Pipeline Research Council International, Inc. (PRCI), 2001. http://dx.doi.org/10.55274/r0011796.

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Experimental work was conducted on two girth welds in large diameter steel pipes. By a proper selection of welding consumables, yield strength mismatch was obtained from 5 to 12% undermatching and 45% overmatching. The girth welds were non-destructively tested using the time of flight diffraction technique by three NDE companies. The verification of the detection and sizing capabilities of TOFD inspection techniques has shown that the performance levels varied widely. This finding confirms the need to validate the NDE techniques. Tensile specimens, Charpy and CTOD specimens, and curved wide pl

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van der Mensbrugghe, Dominique. The META 21 Integrated Assessment Model in GAMS and LHS Sampling. GTAP Working Paper, 2023. http://dx.doi.org/10.21642/gtap.wp95.

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The META 21 Integrated Assessment Model (Dietz et al., 2021) represents a fairly comprehensive climate change simulation model incorporating a number of features: (a) a recent simple climate model; (b) down-scaling of temperature change to the country level; (c) integration of a number of bio-physical tipping points linked to rising temperatures; and (d) economic damages linked to rising sea levels and temperature using recent country-level estimates from the literature. The original implementation of META 21 was in Excel and linked to the @Risk Excel add-in for performing Monte Carlo-type ana

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Bednall, Timothy. Monte Carlo Simulations: Power Analysis and Beyond. Instats Inc., 2023. http://dx.doi.org/10.61700/gpienbvutyd38469.

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This 3-day workshop offers a comprehensive understanding of Monte Carlo simulations and power analysis using Mplus and R software. Tailored for PhD students, professors, and professional researchers across various disciplines, the workshop covers a range of topics from introduction to Monte Carlo simulations, principles of power analysis, to ethical considerations and future directions in the use of these techniques, equipping participants with practical skills and deep understanding of these advanced statistical tools.

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Glaser, R. Monte Carlo simulation of scenario probability distributions. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/632934.

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Xu, S. L., B. Lai, and P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10134610.

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Douglas, L. J. Monte Carlo Simulation as a Research Management Tool. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/1129252.

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Aguayo Navarrete, Estanislao, Austin S. Ankney, Timothy J. Berguson, et al. Monte Carlo Simulation Tool Installation and Operation Guide. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1095434.

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Xu, S. L., B. Lai, and P. J. Viccaro. APS undulator and wiggler sources: Monte-Carlo simulation. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5494991.

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Boyd, Lain D. Monte Carlo Simulation of Radiation in Hypersonic Flows. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada414031.

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Clegg, Benjamin Wyatt, David H. Collins, Jr., and Aparna V. Huzurbazar. Petri Nets for Adversarial Models using Monte Carlo Simulation. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1473775.

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