Relevant bibliographies by topics / Monte Carlo calculation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Monte Carlo calculation'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Monte Carlo calculation"

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Carlson, J., and M. H. Kalos. "Variational Monte Carlo calculation ofO16." Physical Review C 32, no. 6 (1985): 2105–10. http://dx.doi.org/10.1103/physrevc.32.2105.

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Ohtani, Yoshihiko, Mamoru Ohkawa, Akira Uchida, and Tetsuo Yamaya. "Illuminance Calculation Using Monte Carlo Method." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 82, no. 2 (1998): 105–11. http://dx.doi.org/10.2150/jieij1980.82.2_105.

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Li, Deng, Xie Zhongsheng, and Li Shu. "Monte Carlo transport and burnup calculation." Annals of Nuclear Energy 30, no. 1 (2003): 127–32. http://dx.doi.org/10.1016/s0306-4549(02)00044-0.

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OHTANI, Yoshihiko, Mamoru OHKAWA, Akira UCHIDA, and Tetsuo YAMAYA. "Illuminance Calculation Using Monte Carlo Method." Journal of Light & Visual Environment 24, no. 1 (2000): 42–49. http://dx.doi.org/10.2150/jlve.24.1_42.

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Nemnyugin, S. A., and A. M. Petrov. "Monte Carlo calculation of muonic molecules." Computer Physics Communications 97, no. 1-2 (1996): 175–84. http://dx.doi.org/10.1016/0010-4655(96)00030-6.

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Li, Yuan Ying, and De Sheng Zhang. "Plane Truss Reliability Numerical Simulation Based on MATLAB." Applied Mechanics and Materials 256-259 (December 2012): 1091–96. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.1091.

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Based on the basic principles of structure reliability numerical analysis, the numerical simulation of the displacement and stress reliability of plane truss under vertical load was programmed with MATLAB. The failure probability of the most unfavorable structural vertical displacement and stress and reliable indicators were obtained through direct sampling Monte Carlo method, response surface method, response surface-Monte Carlo method and response surface-important sampling Monte Carlo method. It is found that calculation lasts longer since there are so many samples with Monte-Carlo method, higher accuracy and less calculation time can be achieved through response surface-Monte Carlo method and response surface-important sampling Monte Carlo method with fewer samples. The results of different numerical simulation calculations are almost identical and reliable, providing references to reliability analysis of complex structures.
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Ivaschenko, Olena Valerivna. "Multiprocessor modeling technologies for the applied statistical tasks." System technologies 2, no. 127 (2020): 150–63. http://dx.doi.org/10.34185/1562-9945-2-127-2020-12.

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The work considers the multiprocessors technologies of modeling for Monte Carlo tasks. It is shown that only application of the modern super productive systems permitted the new way to realize the mechanism of corresponding partitioned computations. The calculating schemes that supply to provide the increase of productivity and calculations' speed effectiveness are shown. In this article the modified algorithm of parallel calculations is offered based on the Monte Carlo method. Here every calculator has its own random generator of numbers. Thus intermediate calculations come true independently on the different, separately taken blades of cluster , "calculators". The results are already processed on some separately taken master -blades ( "analyzer"). This allows to get rid from the necessary presence of router-communicator between the random generator of numbers and "calculator". Obviously, that such decision allows to accelerate the process of calculations. It is shown that the parallel algorithms of the Monte Carlo method are stable to any input data and have the maximal parallel form and, thus, minimal possible time of realization using the parallel computing devices. If it is possible to appoint one processor to one knot of calculation. Thus the realization of calculations becomes possible in all knots of net area in parallel and simultaneously.
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Shvachych G. G., Sazonova M. S., Zaporozhchenko O. E., Karpova T. P., and Sushko L. F. "MULTIPROCESSOR MODELING TECHNOLOGIES FOR THE APPLIED STATISTICAL TASKS." International Academy Journal Web of Scholar, no. 3(33) (March 31, 2019): 3–9. http://dx.doi.org/10.31435/rsglobal_wos/31032019/6386.

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 The work considers the multiprocessors technologies of modeling for Monte Carlo tasks. It is shown that only application of the modern super productive systems permitted the new way to realize the mechanism of corresponding partitioned computations. The calculating schemes that supply to provide the increase of productivity and calculations' speed effectiveness are shown. In this article the modified algorithm of parallel calculations is offered based on the Monte Carlo method. Here every calculator has its own random generator of numbers. Thus intermediate calculations come true independently on the different, separately taken blades of cluster, "calculators". The results are already processed on some separately taken master -blades ("analyzer"). This allows to get rid from the necessary presence of router-communicator between the random generator of numbers and "calculator". Obviously, that such decision allows to accelerate the process of calculations. It is shown that the parallel algorithms of the Monte Carlo method are stable to any input data and have the maximal parallel form and, thus, minimal possible time of realization using the parallel computing devices. If it is possible to appoint one processor to one knot of calculation. Thus the realization of calculations becomes possible in all knots of net area in parallel and simultaneously.
 
 
 
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Nagasaki, Yoshihito, Yasuko Koga, Ken Anai, and Norio Igawa. "Daylighting calculation by the Monte Carlo method." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 83, Appendix (1999): 100–101. http://dx.doi.org/10.2150/jieij1980.83.appendix_100.

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Peterhans, Matthias, Daniel Frei, Peter Manser, Mauricio Reyes Aguirre, and Michael K. Fix. "Monte Carlo dose calculation on deforming anatomy." Zeitschrift für Medizinische Physik 21, no. 2 (2011): 113–23. http://dx.doi.org/10.1016/j.zemedi.2010.11.002.

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Dissertations / Theses on the topic "Monte Carlo calculation"

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Renaud, Marc-André. "Pre-calculated track Monte Carlo dose calculation engine." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121295.

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Modern treatment planning techniques such as inverse planning have increased the demand for rapid dose calculation methods to accommodate the large number of dose distributions required to generate a treatment plan. General-purpose Monte Carlo approaches for dose calculation are known to offer the highest accuracy in dose calculation at the expense of significant computing time. This work adapts a Macro Monte Carlo approach to dose calculation for electrons andprotons for use with a GPU card, using pre-generated tracks from general-purpose Monte Carlo codes. The algorithm was implemented on the CUDA framework for parallel programming on graphics cards. Comparisons of the algorithm inhomogeneous and inhomogeneous geometry with benchmark Monte Carlo codes yielded agreements within 1% in dose regions of at least 50% of Dmax and up to 3% in low dose regions. A Bragg peak positioning error of less than 1 mm was also observed. Additionally, the limited memory available in commercial graphics cards was overcome by subdividing a mother track bank residing on CPU memory into smaller samples of unique tracks. A method to quantify the latent uncertainty in dose values due to the limited size of a pre-generated track bank was developed. It was shown that the latent uncertainty follows a Poisson distribution as a function of the total number of unique tracks in the track bank. The implementation of the algorithm was found to transport particles in sub-second times per million history for every situation simulated, with speed-ups of 500-2600x for electrons over DOSXYZnrc and 2600-11500x for protons over GEANT4 depending on the particle energies and simulation media.<br>Les techniques modernes de planification de traitement, telle que la planification inverse, ont augmenté la demande pour des méthodes rapides de calcul de dose pour accomoder le grand nombre de distributions de dose requises pour générer un plan de traitement. Les approches Monte Carlo d'usage général sont réputées pour offrir la plus haute précision au calcul de dose au détriment d'une demande plus élevée en temps de calcul. Cet oeuvre revisite une approche MonteCarlo macroscopique pour le calcul de dose avec électrons et protons en utilisant des traques pré-calculées à l'aide de codes Monte Carlo d'usage général. L'approche a été mise en oeuvre avec la plate-forme de programmation CUDA pour le programmage parallèle sur cartes graphiques. Des comparaisons de l'algorithme dans des phantômes homogènes et hétérogènes contre des codes Monte Carlo de référence ont démontré un accord de 1% et 1 mm ou mieux. En outre, les problèmes associés à la basse mémoire disponible dans les cartes graphiques commercial ont été surmontés à l'aide de la méthode de banque mère de traques pré-calculés. Une méthode pour quantifier l'incertitude latente dans les valeurs de dose dû au nombre limité de traques uniques dans la banque de traques a été développée. L'incertitude latente calculée suit une distribution de Poisson en fonction du nombre total de traques unique dans la banque de traques. Finalement, l'algorithme transporte tous les particules en moins d'une seconde pour chaque millions d'historiques dans chaque situation simulée. Un facteur d'accélération de 500-2600x pour le transport d'électrons comparé à DOSXYZnrc et 2600-11500x pour les protons comparé à GEANT4 a été observé, dépendamment de l'énergie des particules et de l'environnement dans lequel les particules sont transportées.
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Lee, Li-Chyn 1965. "Comparison of Monte Carlo and analytic critical area calculation." Thesis, The University of Arizona, 1992. http://hdl.handle.net/10150/278175.

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Since the profitability of VLSI industries is related to yield, the IC manufacturer finds it highly desirable to be able to predict the yield by computer-aided methods. A key part in the procedure to obtain yield by computer simulation is to find the critical area of a layout. This thesis is primarily devoted to the calculations of critical area. There are two techniques to find the critical area. In the first technique, an analytic method was used to analyze the circuit geometry in order to find the critical area. In the second technique a Monte Carlo Method is used. A program using this Monte Carlo yield simulation (the main method used in this thesis) has been developed for determining critical area of the metal layer of a 4K random access memory. The analytic method is used in a supporting way. The thesis also proposes an easy method to process the vast amount of layout database. This method reduces the time consumed by Monte Carlo simulation.
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Kawasaki, Kazunori Sturtevant Bradford. "Monte Carlo calculation of the flow of granular materials /." Diss., Pasadena, Calif. : California Institute of Technology, 1986. http://resolver.caltech.edu/CaltechETD:etd-11092007-110816.

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Engdahl, Staffan. "Validation of Ion Therepy Dose Calculation Algorithms by Monte Carlo." Thesis, KTH, Fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170409.

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Khan, Nadeem. "Dosimetric Calculation of a Thermo Brachytherapy Seed: A Monte Carlo Study." Connect to full text in OhioLINK ETD Center, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=mco1228860927.

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Thesis (M.S.)--University of Toledo, 2008.<br>"In partial fulfillment of the requirements for the degree of Master of Science in Biomedical Sciences." Title from title page of PDF document. Bibliography: p. 153-155.
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Van, der Merwe Carel Johannes. "Calculation aspects of the European Rebalanced Basket Option using Monte Carlo methods." Thesis, Stellenbosch : University of Stellenbosch, 2010. http://hdl.handle.net/10019.1/5190.

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Thesis (MComm (Statistics and Actuarial Science)--University of Stellenbosch, 2010.<br>ENGLISH ABSTRACT: Life insurance and pension funds offer a wide range of products that are invested in a mix of assets. These portfolios (II), underlying the products, are rebalanced back to predetermined fixed proportions on a regular basis. This is done by selling the better performing assets and buying the worse performing assets. Life insurance or pension fund contracts can offer the client a minimum payout guarantee on the contract by charging them an extra premium (a). This problem can be changed to that of the pricing of a put option with underlying . It forms a liability for the insurance firm, and therefore needs to be managed in terms of risks as well. This can be done by studying the option’s sensitivities. In this thesis the premium and sensitivities of this put option are calculated, using different Monte Carlo methods, in order to find the most efficient method. Using general Monte Carlo methods, a simplistic pricing method is found which is refined by applying mathematical techniques so that the computational time is reduced significantly. After considering Antithetic Variables, Control Variates and Latin Hypercube Sampling as variance reduction techniques, option prices as Control Variates prove to reduce the error of the refined method most efficiently. This is improved by considering different Quasi-Monte Carlo techniques, namely Halton, Faure, normal Sobol’ and other randomised Sobol’ sequences. Owen and Faure-Tezuke type randomised Sobol’ sequences improved the convergence of the estimator the most efficiently. Furthermore, the best methods between Pathwise Derivatives Estimates and Finite Difference Approximations for estimating sensitivities of this option are found. Therefore by using the refined pricing method with option prices as Control Variates together with Owen and Faure-Tezuke type randomised Sobol’ sequences as a Quasi-Monte Carlo method, more efficient methods to price this option (compared to simplistic Monte Carlo methods) are obtained. In addition, more efficient sensitivity estimators are obtained to help manage risks.<br>AFRIKAANSE OPSOMMING: Lewensversekering en pensioenfondse bied die mark ’n wye reeks produkte wat belê word in ’n mengsel van bates. Hierdie portefeuljes (II), onderliggend aan die produkte, word op ’n gereelde basis terug herbalanseer volgens voorafbepaalde vaste proporsies. Dit word gedoen deur bates wat beter opbrengste gehad het te verkoop, en bates met swakker opbrengste aan te koop. Lewensversekeringof pensioenfondskontrakte kan ’n kliënt ’n verdere minimum uitbetaling aan die einde van die kontrak waarborg deur ’n ekstra premie (a) op die kontrak te vra. Die probleem kan verander word na die prysing van ’n verkoopopsie met onderliggende bate . Hierdie vorm deel van die versekeringsmaatskappy se laste en moet dus ook bestuur word in terme van sy risiko’s. Dit kan gedoen word deur die opsie se sensitiwiteite te bestudeer. In hierdie tesis word die premie en sensitiwiteite van die verkoopopsie met behulp van verskillende Monte Carlo metodes bereken, om sodoende die effektiefste metode te vind. Deur die gebruik van algemene Monte Carlo metodes word ’n simplistiese prysingsmetode, wat verfyn is met behulp van wiskundige tegnieke wat die berekeningstyd wesenlik verminder, gevind. Nadat Antitetiese Veranderlikes, Kontrole Variate en Latynse Hiperkubus Steekproefneming as variansiereduksietegnieke oorweeg is, word gevind dat die verfynde metode se fout die effektiefste verminder met behulp van opsiepryse as Kontrole Variate. Dit word verbeter deur verskillende Quasi-Monte Carlo tegnieke, naamlik Halton, Faure, normale Sobol’ en ander verewekansigde Sobol’ reekse, te vergelyk. Die Owen en Faure-Tezuke tipe verewekansigde Sobol’ reeks verbeter die konvergensie van die beramer die effektiefste. Verder is die beste metode tussen Baanafhanklike Afgeleide Beramers en Eindige Differensie Benaderings om die sensitiwiteit vir die opsie te bepaal, ook gevind. Deur dus die verfynde prysingsmetode met opsiepryse as Kontrole Variate, saam met Owen en Faure-Tezuke tipe verewekansigde Sobol’ reekse as ’n Quasi-Monte Carlo metode te gebruik, word meer effektiewe metodes om die opsie te prys, gevind (in vergelyking met simplistiese Monte Carlo metodes). Verder is meer effektiewe sensitiwiteitsberamers as voorheen gevind wat gebruik kan word om risiko’s te help bestuur.
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Fragoso, Margarida. "Application of Monte Carlo techniques for the calculation of accurate brachytherapy dose distributions." Thesis, Institute of Cancer Research (University Of London), 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413609.

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Johns, Dewi. "Radiotherapy dose calculation in oesophageal cancer : comparison of analytical and Monte Carlo methods." Thesis, Cardiff University, 2016. http://orca.cf.ac.uk/105551/.

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In this work a distributed computing system (RTGrid) has been configured and deployed to provide a statistically robust comparison of Monte Carlo (MC) and analytical dose calculations. 52 clinical oesophageal radiotherapy plans were retrospectively re-calculated using the Pencil Beam Enhanced (PBE) and Collapsed Cone Enhanced (CCE) algorithm within the Oncentra v4.3 radiotherapy (RT) Treatment Planning System (TPS). Simulations were performed using the BEAMnrc and DOSXYZnrc codes. The Computing Environment for Radiotherapy Research (CERR) has been used to calculate Dose Volume Histogram (DVH) parameters such as the volume receiving 95% Dose for the Planning Target Volume (PTV) for the PBE, CCE and MC calculated dose distributions. An initial sample of 12 oesophageal radiotherapy treatment plans were simulated using the RTGrid system. The differences in the DVH parameters between the dose calculation methods, and the variance in the 12 cases, were used to calculate the sample size needed. The required sample size was determined to be 37, so a further 40 oesophageal cases were simulated, following the same method. The median difference in the PTV V95% between CCE and MC in the group of 40 cases was found to be 3%. To choose a suitable test for the statistical significance of the difference, the Shapiro-Wilk test was performed, which showed that the differences between the two sets of PTV V95% values did not follow a Gaussian. Therefore the Wilcoxon matched pairs test was indicated, which showed that the null hypothesis (i.e. that the distributions are the same) was rejected with a p-value less than 0.001, so there is very strong evidence for a difference in the two sets of values of PTV V95%. Similar statistical analyses were performed for other DVH parameters, as well as Conformance Indices used to describe the agreement between the 95% dose and the PTV, and estimates of the Tumour Control Probability (TCP). From the results, the use of MC simulations are recommended when non-soft tissue voxels make up > 60% of the PTV.
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Matsuyama, Akinobu. "Study on Monte-Carlo Calculation of Neoclassical Transport Matrix in Nonaxisymmetric Toroidal Plasmas." Kyoto University, 2010. http://hdl.handle.net/2433/120410.

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Heath, Emily C. "Evaluation of the PEREGRINE Monte Carlo dose calculation code for 6 MV photon beams." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=19418.

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The accuracy of conventional dose calculation algorithms employed in external photonbeam treatment planning is limited by their inability to fully model the radiation source andaccount for the electron fluence perturbations which occur in regions of non-uniform density.In this work, we evaluated the dosimetric accuracy of the PEREGRINE Monte Carlo dosecalculation code for 6 MV photons. Dose profiles calculated by PEREGRINE werecompared with measurements in homogeneous and heterogeneous phantoms. A comparisonof dose profiles calculated by PEREGRINE and the EGSnrc Monte Carlo code was alsoperformed. To fully model the Varian Millennium 120 leaf collimator, a new MLCcomponent module was developed for BEAMnrc.Overall, the agreement between PEREGRINE and measurements and EGSnrc is within1% with the exception of the buildup region, where the accuracy of the beam model isaffected by an artificially increased electron subsource weight, and for 30x30 cm2 fieldswhere the beam model does not accurately predict the off-axis fluence. A clinical comparisonof IMRT dose distributions calculated by PEREGRINE and the CORVUS pencil beamalgorithm indicates that the heterogeneity correction implemented in CORVUSunderestimates the dose received by sensitive structures, receiving up to 20% of theprescribed dose, which lie in the vicinity of low density tissues.
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Books on the topic "Monte Carlo calculation"

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Albers, John. Results of the Monte Carlo calculation of one-and two-dimensional distributions of particles and damage: Ion implanteddopants in silicon. National Bureau of Standards, 1987.

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Albers, John. Results of the Monte Carlo calculation of one- and two-dimensional distributions of particles and damage: Ion implanted dopants in silicon. U.S. Dept. of Commerce, National Bureau of Standards, 1987.

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Till, E. Calculation of the radiation transport in rock salt using Monte Carlo methods: Final report (HAW Project). GSF-Forschungszentrum für Umwelt und Gesundheit, Institut für Strahlenschutz, 1994.

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László, Koblinger, ed. Monte Carlo particle transport methods: Neutron and photon calculations. CRC Press, 1991.

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Zhang, Shiwei. Exact Monte Carlo calculations for fermions on a parallel machine. Cornell Theory Center, Cornell University, 1993.

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Filippi, Claudia. Multiconfiguration wavefunctions for quantum Monte Carlo calculations of first-row diatomic molecules. Cornell Theory Center, Cornell University, 1996.

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Mehta, Shailesh. The Monte Carlo approach to calculating radial distribution functions in dense plasmas. University of Birmingham, 1995.

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Hulse, Paul. Applying genetic algorithms to importance map generation for Monte Carlo tracking shielding calculations. University of Salford, 1994.

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Jansen, Jan Theo Maria. Monte Carlo calculations in diagnostic radiology: Dose conversion factors and risk benefit analyses = Monte Carlo berekeningen in de radiodiagnostiek : dosis conversiefactoren en risico baten analyses. Rijkuniversiteit te Leiden, 1998.

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Whitlock, Paula A. Green's function Monte Carlo calculations for 4He using the shadow wave function as importance function. Cornell Theory Center, Cornell University, 1990.

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Book chapters on the topic "Monte Carlo calculation"

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Frieden, B. Roy. "The Monte Carlo Calculation." In Probability, Statistical Optics, and Data Testing. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56699-8_7.

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Frieden, B. Roy. "The Monte Carlo Calculation." In Probability, Statistical Optics, and Data Testing. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-97289-8_7.

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Fischetti, M. V., and J. M. Higman. "Theory and Calculation of the Deformation Potential Electron-Phonon Scattering Rates in Semiconductors." In Monte Carlo Device Simulation. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7_5.

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March, N. H. "Quantum Monte Carlo Calculation of Correlation Energy." In Electron Correlation in Molecules and Condensed Phases. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-1370-8_5.

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Nightingale, M. P., and R. G. Caflisch. "Monte Carlo Calculation of Transfer Matrix Eigenvalues." In Springer Proceedings in Physics. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-93400-1_22.

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Shirley, Peter, and Changyaw Wang. "Direct Lighting Calculation by Monte Carlo Integration." In Photorealistic Rendering in Computer Graphics. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-57963-9_6.

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Ito, Akira. "Three-Dimensional Dose Calculation for Total Body Irradiation." In Monte Carlo Transport of Electrons and Photons. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1059-4_27.

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Kuznetsov, Anatoly, Irina Melnikova, Dmitriy Pozdnyakov, Olga Seroukhova, and Alexander Vasilyev. "Monte-Carlo Method for the Solar Irradiance Calculation." In Remote Sensing of the Environment and Radiation Transfer. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14899-6_13.

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Deng, Jun, Steve B. Jiang, Todd Pawlicki, Jinsheng Li, and C. M. Ma. "Electron Beam Commissioning for Monte Carlo Dose Calculation." In The Use of Computers in Radiation Therapy. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59758-9_163.

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Pisaturo, O., R. Moeckli, and F. O. Bochud. "Monte Carlo based independent monitor unit calculation in IMRT." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03474-9_123.

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Conference papers on the topic "Monte Carlo calculation"

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Xu, Qi, Ganglin Yu, and Kan Wang. "Research on GPU Acceleration for Monte Carlo Criticality Calculation." In SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, edited by D. Caruge, C. Calvin, C. M. Diop, F. Malvagi, and J. C. Trama. EDP Sciences, 2014. http://dx.doi.org/10.1051/snamc/201404210.

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Wu, Jieqiong, Jianping Li, Dewu Xie, and Fengjiao Fan. "The Monte Carlo calculation method of multiple integration." In 2014 11th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2014. http://dx.doi.org/10.1109/iccwamtip.2014.7073457.

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Abniki, Hassan, and Saeed Nateghi. "Voltage sag calculation based on Monte Carlo technique." In 2012 11th International Conference on Environment and Electrical Engineering (EEEIC). IEEE, 2012. http://dx.doi.org/10.1109/eeeic.2012.6221458.

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Li, Ze-guang, Kan Wang, and Gang-lin Yu. "Research on Monte Carlo Perturbation Calculation Methods Applied in Reactor Physics." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75584.

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In the reactor design and analysis, there is often a need to calculate the effects caused by perturbations of temperature, components and even structure of reactors on reactivity. And in sensitivity studies, uncertainty analysis of target quantities and unclear data adjustment, perturbation calculations are also widely used. To meet the need of different types of reactors (complex, multidimensional systems), Monte Carlo perturbation methods have been developed. In this paper, several kinds of perturbation methods are investigated. Specially, differential operator sampling method and correlated tracking method are discussed in details. MCNP’s perturbation calculation capability is discussed by calculating certain problems, from which some conclusions are obtained on the capabilities of the differential operator sampling method used in the perturbation calculation model of MCNP. Also, a code using correlated tracking method has been developed to solve certain problems with cross-section changes, and the results generated by this code agree with the results generated by straightforward Monte Carlo techniques.
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Adrien, Camille, Mercedes Lòpez Noriega, Guillaume Bonniaud, Jean-Marc Bordy, Cindy Le Loirec, and Bénédicte Poumarede. "Monte Carlo PENRADIO software for dose calculation in medical imaging." In SNA + MC 2013 - Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, edited by D. Caruge, C. Calvin, C. M. Diop, F. Malvagi, and J. C. Trama. EDP Sciences, 2014. http://dx.doi.org/10.1051/snamc/201401601.

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Fu, Jiaqi, Jingfeng Bai, Yanfang Liu, and Cheng Ni. "Fast Monte Carlo dose calculation based on deep learning." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263502.

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Jafari, Hamid, and Majid Shahriari. "Neutron Radiography System Collimator Design via Monte Carlo Calculation." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-29745.

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Neutron radiography uses the unique interaction probabilities of neutrons to create images of materials. This imaging technique is non-destructive. MCNP Monte Carlo Code has been used to design an optimized neutron radiography system that utilizes 241Am-Be neutron source. Many different arrangements have been simulated to obtain a neutron flux with higher amplitude and more uniform distribution in the collimator outlet, next to image plane. In the final arrangement the specifications of neutron filter, Gamma-ray shield and beam collimator has been determined. Simulations has been Carried out for a 5Ci 241Am-Be neutron source. In this case 43.8 n/cm2s thermal neutron flux has been achieved at a distance of 35cm from neutron source.
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Karishy, Slyman, Christophe Palermo, Giulio Sabatini, et al. "Monte Carlo calculation of In0.53Ga0.47As and InAs noise parameters." In 2017 International Conference on Noise and Fluctuations (ICNF). IEEE, 2017. http://dx.doi.org/10.1109/icnf.2017.7985941.

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Kosobutskyy, Petro, Andrii Kovalchuk, M. Kuzmynykh, and M. Shvarts. "Geometric calculation of Pi using the Monte Carlo method." In 2016 XII International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH). IEEE, 2016. http://dx.doi.org/10.1109/memstech.2016.7507538.

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Gao, Yajing, Ming Zhou, and Gengyin Li. "Sequential Monte Carlo Simulation Based Available Transfer Capability Calculation." In 2006 International Conference on Power System Technology. IEEE, 2006. http://dx.doi.org/10.1109/icpst.2006.321841.

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Reports on the topic "Monte Carlo calculation"

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Roesler, Stefan. Monte Carlo Calculation of the Radiation Field at Aircraft Altitudes. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/787221.

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Kim, Kimin, Jong-Kyu Park, Gerrit Kramer, and Allen H. Boozer. Delta f Monte Carlo Calculation Of Neoclassical Transport In Perturbed Tokamaks. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1063119.

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Moin-Vasiri, M. Monte Carlo calculation of skyshine'' neutron dose from ALS (Advanced Light Source). Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6442833.

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Fasso, Alberto. A Monte Carlo Calculation of Muon Flux at Ground Level from Primary Cosmic Gamma Rays. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/10509.

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Albers, John. Results of the Monte Carlo calculation of one- and two-dimensional distributions of particles and damage. National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.sp.400-79.

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Schach von Wittenau, A. E., L. J. Cox, P. M. Jr Bergstrom, et al. Treatment of patient-dependent beam modifiers in photon treatments by the Monte Carlo dose calculation code PEREGRINE. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/490473.

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Picard, Richard Roy, Anthony J. Zukaitis, and Robert Authur Forster, III. Evaluating Equivalent Monte Carlo Calculations. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1475321.

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Dewberry, R. Proposal to Acquire Experimental Data and to Model the Results with a Monte Carlo Calculation of a Secondary Source Correction Factor for Area Source Acquisitions of Holdup y-PHA Measurements. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/807914.

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Bichsel, H. Monte Carlo calculations of track structures. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/244506.

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Williams, Timothy J., Ramesh Balakrishnan, Steven C. Pieper, et al. Quantum Monte Carlo Calculations in Nuclear Theory. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1483999.

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