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Добірка наукової літератури з теми "Monte-Carlo numerical simulation"

Автор: Grafiati

Опубліковано: 25 січня 2025

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Статті в журналах з теми "Monte-Carlo numerical simulation"

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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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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Price, Thomas E., and D. P. Story. "Monte Carlo Simulation of Numerical Integration." Journal of Statistical Computation and Simulation 23, no. 1-2 (December 1985): 97–112. http://dx.doi.org/10.1080/00949658508810860.

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Cheng, Minqi, and Jiasheng Guo. "Analysis of the Principle and Two Applications for Monte-Carlo Simulations." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 136–41. http://dx.doi.org/10.54097/3dg18k50.

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As a matter of fact, stochastic process and sampling algorithms are widely used in the state-of-art numerical simulations. In order to evaluate the random effect, the means of Monte-Carlo simulations are widely adopted and used to obtain a convergence or trending results. With this in mind, this essay mainly talks about the two applications of Monte Carlo simulation and the impact of it toward the society and human race. To be specific, firstly, the origin of Monte-Carlo simulation was revealed and its history of development was elaborated. After that, the basic concept of Monte-Carlo analysis was formulated as well as the sampling process of it is done briefly. All those foreshadows were aimed at assisting the readers to obtain a basic idea of this simulating method and be able to comprehend the relatively sophisticated applications, including financial and computer science knowledge. Overall, these results shed light on guiding further exploration of Monte-Carlo simulations.

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Mo, Wen Hui. "Monte Carlo Simulation of Reliability for Gear." Advanced Materials Research 268-270 (July 2011): 42–45. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.42.

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Production errors, material properties and applied loads of the gear are stochastic .Considering the influence of these stochastic factors, reliability of gear is studied. The sensitivity analysis of random variable can reduce the number of random variables. Simulating random variables, a lot of samples are generated. Using the Monte Carlo simulation based on the sensitivity analysis, reliabilities of contacting fatigue strength and bending fatigue strength can be obtained. The Monte Carlo simulation approaches the accurate solution gradually with the increase of the number of simulations. The numerical example validates the proposed method.

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Caflisch, Russel E. "Monte Carlo and quasi-Monte Carlo methods." Acta Numerica 7 (January 1998): 1–49. http://dx.doi.org/10.1017/s0962492900002804.

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Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasi-random (also called low-discrepancy) sequences, which are a deterministic alternative to random or pseudo-random sequences. The points in a quasi-random sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasi-Monte Carlo, has a convergence rate of approximately O((logN)kN−1). For quasi-Monte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less than the fluid dynamic length scale. Computational examples are presented throughout the text to illustrate the theory. A number of open problems are described.

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Sheet, Abd Al Kareem I., and Nadia Adeel Saeed. "Monte Carlo Simulation and Applications." Journal of Kufa for Mathematics and Computer 1, no. 6 (December 30, 2012): 75–78. https://doi.org/10.31642/jokmc/2018/010608.

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The Monte Carlo simulation technique is one of the common computational tools used to imitate and follow up complex real life systems and their development with time. Variables of a disease problem were defined and the mathematical model for this problem was constructed. The numerical solution of this model was compared with the computational simulation of 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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MATUTTIS, HANS-GEORG, and NOBUYASU ITO. "NONEXISTENCE OF d-WAVE-SUPERCONDUCTIVITY IN THE QUANTUM MONTE CARLO SIMULATION OF THE HUBBARD MODEL." International Journal of Modern Physics C 16, no. 06 (June 2005): 857–66. http://dx.doi.org/10.1142/s0129183105007571.

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For the existence of d-wave-superconductivity in the Hubbard model, previous quantum Monte Carlo results by other authors, which showed a power law increase of the d-wave susceptibility, seem to contradict a recently published theorem. We show those quantum Monte Carlo calculations were numerically contaminated, analyze the numerical problem and propose a numerically more stable computing scheme.

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Zhang, Xiaobo, Zhenzhou Lu, Kai Cheng, and Yanping Wang. "A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 4 (February 12, 2020): 622–35. http://dx.doi.org/10.1177/1748006x19899504.

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Local reliability sensitivity and global reliability sensitivity are required in reliability-based design optimization, since they can provide rich information including variable importance ranking and gradient information. However, traditional Monte Carlo simulation is inefficient for engineering application. A novel numerical simulation method based on Monte Carlo simulation and directional sampling is proposed to simultaneously estimate local reliability sensitivity and global reliability sensitivity. By suitable transformation, local reliability sensitivity and global reliability sensitivity can be estimated simultaneously as by-products of reliability analysis for Monte Carlo simulation method. The key is how to efficiently classify Monte Carlo simulation samples into two categories: failure samples and safety samples. Directional sampling method, a classical reliability analysis method, is more efficient than Monte Carlo simulation for reliability analysis. A novel strategy based on nearest Euclidean distance is proposed to approximately screen out failure samples from Monte Carlo simulation samples using directional sampling samples. In the proposed method, local reliability sensitivity and global reliability sensitivity are by-products of reliability analysis using the directional sampling method. Different from existing methods, the proposed method does not introduce hypotheses and does not require additional gradient information. The advantages of the Monte Carlo simulation and directional sampling are well integrated in the proposed method. The accuracy and the efficiency of the proposed method for local reliability sensitivity and global reliability sensitivity are demonstrated by four numerical examples and two engineering examples including the headless rivet and the wing box structure.

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Casella, Bruno, and Gareth O. Roberts. "Exact Monte Carlo simulation of killed diffusions." Advances in Applied Probability 40, no. 1 (March 2008): 273–91. http://dx.doi.org/10.1239/aap/1208358896.

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We describe and implement a novel methodology for Monte Carlo simulation of one-dimensional killed diffusions. The proposed estimators represent an unbiased and efficient alternative to current Monte Carlo estimators based on discretization methods for the cases when the finite-dimensional distributions of the process are unknown. For barrier option pricing in finance, we design a suitable Monte Carlo algorithm both for the single barrier case and the double barrier case. Results from numerical investigations are in excellent agreement with the theoretical predictions.

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Дисертації з теми "Monte-Carlo numerical simulation"

Lloyd, Jennifer A. "Numerical methods for Monte Carlo device simulation." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/12766.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1992.
Includes bibliographical references (leaves 51-53).
by Jennifer Anne Lloyd.
M.S.

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Furrer, Marc. "Numerical Accuracy of Least Squares Monte Carlo." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/01650217002/$FILE/01650217002.pdf.

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Srinivasan, Raghuram. "Monte Carlo Alternate Approaches to Statistical Performance Estimation in VLSI Circuits." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531763.

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Peter, Felix. "A quantitative comparison of numerical option pricing techniques." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/01592823001/$FILE/01592823001.pdf.

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Creffield, Charles Edward. "The application of numerical techniques to models of strongly correlated electrons." Thesis, King's College London (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266066.

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Fakhereddine, Rana. "Méthodes de Monte Carlo stratifiées pour l'intégration numérique et la simulation numériques." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM047/document.

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Les méthodes de Monte Carlo (MC) sont des méthodes numériques qui utilisent des nombres aléatoires pour résoudre avec des ordinateurs des problèmes des sciences appliquées et des techniques. On estime une quantité par des évaluations répétées utilisant N valeurs et l'erreur de la méthode est approchée par la variance de l'estimateur. Le présent travail analyse des méthodes de réduction de la variance et examine leur efficacité pour l'intégration numérique et la résolution d'équations différentielles et intégrales. Nous présentons d'abord les méthodes MC stratifiées et les méthodes d'échantillonnage par hypercube latin (LHS : Latin Hypercube Sampling). Parmi les méthodes de stratification, nous privilégions la méthode simple (MCS) : l'hypercube unité Is := [0; 1)s est divisé en N sous-cubes d'égale mesure, et un point aléatoire est choisi dans chacun des sous-cubes. Nous analysons la variance de ces méthodes pour le problème de la quadrature numérique. Nous étudions particulièrment le cas de l'estimation de la mesure d'un sous-ensemble de Is. La variance de la méthode MCS peut être majorée par O(1=N1+1=s). Les résultats d'expériences numériques en dimensions 2,3 et 4 montrent que les majorations obtenues sont précises. Nous proposons ensuite une méthode hybride entre MCS et LHS, qui possède les propriétés de ces deux techniques, avec un point aléatoire dans chaque sous-cube et les projections des points sur chacun des axes de coordonnées également réparties de manière régulière : une projection dans chacun des N sousintervalles qui divisent I := [0; 1) uniformément. Cette technique est appelée Stratification Sudoku (SS). Dans le même cadre d'analyse que précédemment, nous montrons que la variance de la méthode SS est majorée par O(1=N1+1=s) ; des expériences numériques en dimensions 2,3 et 4 valident les majorations démontrées. Nous présentons ensuite une approche de la méthode de marche aléatoire utilisant les techniques de réduction de variance précédentes. Nous proposons un algorithme de résolution de l'équation de diffusion, avec un coefficient de diffusion constant ou non-constant en espace. On utilise des particules échantillonnées suivant la distribution initiale, qui effectuent un déplacement gaussien à chaque pas de temps. On ordonne les particules suivant leur position à chaque étape et on remplace les nombres aléatoires qui permettent de calculer les déplacements par les points stratifiés utilisés précédemment. On évalue l'amélioration apportée par cette technique sur des exemples numériques Nous utilisons finalement une approche analogue pour la résolution numérique de l'équation de coagulation, qui modélise l'évolution de la taille de particules pouvant s'agglomérer. Les particules sont d'abord échantillonnées suivant la distribution initiale des tailles. On choisit un pas de temps et, à chaque étape et pour chaque particule, on choisit au hasard un partenaire de coalescence et un nombre aléatoire qui décide de cette coalescence. Si l'on classe les particules suivant leur taille à chaque pas de temps et si l'on remplace les nombres aléatoires par des points stratifiés, on observe une réduction de variance par rapport à l'algorithme MC usuel
Monte Carlo (MC) methods are numerical methods using random numbers to solve on computers problems from applied sciences and techniques. One estimates a quantity by repeated evaluations using N values ; the error of the method is approximated through the variance of the estimator. In the present work, we analyze variance reduction methods and we test their efficiency for numerical integration and for solving differential or integral equations. First, we present stratified MC methods and Latin Hypercube Sampling (LHS) technique. Among stratification strategies, we focus on the simple approach (MCS) : the unit hypercube Is := [0; 1)s is divided into N subcubes having the same measure, and one random point is chosen in each subcube. We analyze the variance of the method for the problem of numerical quadrature. The case of the evaluation of the measure of a subset of Is is particularly detailed. The variance of the MCS method may be bounded by O(1=N1+1=s). The results of numerical experiments in dimensions 2,3, and 4 show that the upper bounds are tight. We next propose an hybrid method between MCS and LHS, that has properties of both approaches, with one random point in each subcube and such that the projections of the points on each coordinate axis are also evenly distributed : one projection in each of the N subintervals that uniformly divide the unit interval I := [0; 1). We call this technique Sudoku Sampling (SS). Conducting the same analysis as before, we show that the variance of the SS method is bounded by O(1=N1+1=s) ; the order of the bound is validated through the results of numerical experiments in dimensions 2,3, and 4. Next, we present an approach of the random walk method using the variance reduction techniques previously analyzed. We propose an algorithm for solving the diffusion equation with a constant or spatially-varying diffusion coefficient. One uses particles, that are sampled from the initial distribution ; they are subject to a Gaussian move in each time step. The particles are renumbered according to their positions in every step and the random numbers which give the displacements are replaced by the stratified points used above. The improvement brought by this technique is evaluated in numerical experiments. An analogous approach is finally used for numerically solving the coagulation equation ; this equation models the evolution of the sizes of particles that may agglomerate. The particles are first sampled from the initial size distribution. A time step is fixed and, in every step and for each particle, a coalescence partner is chosen and a random number decides if coalescence occurs. If the particles are ordered in every time step by increasing sizes an if the random numbers are replaced by statified points, a variance reduction is observed, when compared to the results of usual MC algorithm

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Haber, René. "Numerical methods for density of states calculations." [S.l. : s.n.], 2008.

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Zhang, Yan. "Weakly first-order phase transitions : [epsilon] expansion vs. numerical simulation /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9715.

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Nghiem, Thi Thu Trang. "Numerical study of electro-thermal effects in silicon devices." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00827633.

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The ultra-short gate (LG < 20 nm) CMOS components (Complementary Metal-Oxide-Semiconductor) face thermal limitations due to significant local heating induced by phonon emission by hot carriers in active regions of reduced size. This phenomenon, called self-heating effect, is identified as one of the most critical for the continuous increase in the integration density of circuits. This is especially crucial in SOI technology (silicon on insulator), where the presence of the buried insulator hinders the dissipation of heat.At the nanoscale, the theoretical study of these heating phenomena, which cannot be led using the macroscopic models (heat diffusion coefficient), requires a detailed microscopic description of heat transfers that are locally non-equilibrium. It is therefore appropriate to model, not only the electron transport and the phonon generation, but also the phonon transport and the phonon-phonon and electron-phonon interactions. The formalism of the Boltzmann transport equation (BTE) is very suitable to study this problem. In fact, it is widely used for years to study the transport of charged particles in semiconductor components. This formalism is much less standard to study the transport of phonons. One of the problems of this work concerns the coupling of the phonon BTE with the electron transport.In this context, wse have developed an algorithm to calculate the transport of phonons by the direct solution of the phonon BTE. This algorithm of phonon transport was coupled with the electron transport simulated by the simulator "MONACO" based on a statistical (Monte Carlo) solution of the BTE. Finally, this new electro-thermal simulator was used to study the self-heating effects in nano-transistors. The main interest of this work is to provide an analysis of electro-thermal transport beyond a macroscopic approach (Fourier formalism for thermal transport and the drift-diffusion approach for electric current, respectively). Indeed, it provides access to the distributions of phonons in the device for each phonon mode. In particular, the simulator provides a better understanding of the hot electron effects at the hot spots and of the electron relaxation in the access.

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Burgos, Sylvestre Jean-Baptiste Louis. "The computation of Greeks with multilevel Monte Carlo." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:6453a93b-9daf-4bfe-8c77-9cd6802f77dd.

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In mathematical finance, the sensitivities of option prices to various market parameters, also known as the “Greeks”, reflect the exposure to different sources of risk. Computing these is essential to predict the impact of market moves on portfolios and to hedge them adequately. This is commonly done using Monte Carlo simulations. However, obtaining accurate estimates of the Greeks can be computationally costly. Multilevel Monte Carlo offers complexity improvements over standard Monte Carlo techniques. However the idea has never been used for the computation of Greeks. In this work we answer the following questions: can multilevel Monte Carlo be useful in this setting? If so, how can we construct efficient estimators? Finally, what computational savings can we expect from these new estimators? We develop multilevel Monte Carlo estimators for the Greeks of a range of options: European options with Lipschitz payoffs (e.g. call options), European options with discontinuous payoffs (e.g. digital options), Asian options, barrier options and lookback options. Special care is taken to construct efficient estimators for non-smooth and exotic payoffs. We obtain numerical results that demonstrate the computational benefits of our algorithms. We discuss the issues of convergence of pathwise sensitivities estimators. We show rigorously that the differentiation of common discretisation schemes for Ito processes does result in satisfactory estimators of the the exact solutions’ sensitivities. We also prove that pathwise sensitivities estimators can be used under some regularity conditions to compute the Greeks of options whose underlying asset’s price is modelled as an Ito process. We present several important results on the moments of the solutions of stochastic differential equations and their discretisations as well as the principles of the so-called “extreme path analysis”. We use these to develop a rigorous analysis of the complexity of the multilevel Monte Carlo Greeks estimators constructed earlier. The resulting complexity bounds appear to be sharp and prove that our multilevel algorithms are more efficient than those derived from standard Monte Carlo.

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Книги з теми "Monte-Carlo numerical simulation"

Pierre, L' Ecuyer, and Owen Art B, eds. Monte Carlo and quasi-Monte Carlo methods 2008. Heidelberg: Springer, 2009.

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Tomizawa, Kazutaka. Numerical simulation of submicron semiconductor devices. Boston: Artech House, 1993.

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Binder, Kurt. Monte Carlo Simulation in Statistical Physics: An Introduction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002.

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W, Heermann Dieter, ed. Monte Carlo simulation in statistical physics: An introduction. 5th ed. Heidelberg: Springer, 2010.

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Schwarm, Fritz-Walter. Monte Carlo Simulation of Cyclotron Lines in Strong Magnetic Fields - Theory and Application. Erlangen: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2017.

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1955-, Privman V., ed. Finite size scaling and numerical simulation of statistical systems. Singapore: World Scientific, 1990.

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George, Casella, and SpringerLink (Online service), eds. Introducing Monte Carlo Methods with R. New York, NY: Springer Science+Business Media, LLC, 2010.

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Górski, Jarosław. Non-linear models of structures with random geometric and material imperfactions [sic] simulation-based approach. Gdańsk: Wydawn. Politechniki Gdańskiej, 2006.

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Jayashree, Moorthy, and Langley Research Center, eds. Numerical simulation of the nonlinear response of composite plates under combined thermal and acoustic loading: Final report, for the period ended March 15, 1995. Norfolk, Va: Old Dominion University, 1995.

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Частини книг з теми "Monte-Carlo numerical simulation"

Laux, Steven E., and Massimo V. Fischetti. "Numerical Aspects and Implementation of theDamoclesMonte Carlo Device Simulation Program." In Monte Carlo Device Simulation, 1–26. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4026-7_1.

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Andrieu, Christophe, Arnaud Doucet, and Roman Holenstein. "Particle Markov Chain Monte Carlo for Efficient Numerical Simulation." In Monte Carlo and Quasi-Monte Carlo Methods 2008, 45–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04107-5_3.

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Hoel, Håkon, Erik von Schwerin, Anders Szepessy, and Raúl Tempone. "Adaptive Multilevel Monte Carlo Simulation." In Numerical Analysis of Multiscale Computations, 217–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21943-6_10.

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Lécot, Christian, Moussa Tembely, Arthur Soucemarianadin, and Ali Tarhini. "Numerical Simulation of the Drop Size Distribution in a Spray." In Monte Carlo and Quasi-Monte Carlo Methods 2010, 523–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27440-4_30.

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Chan, Raymond H., Yves ZY Guo, Spike T. Lee, and Xun Li. "Numerical Method (1): Monte Carlo Simulation." In Financial Mathematics, Derivatives and Structured Products, 255–75. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9534-9_21.

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Platen, Eckhard, and Nicola Bruti-Liberati. "Monte Carlo Simulation of SDEs." In Numerical Solution of Stochastic Differential Equations with Jumps in Finance, 477–505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13694-8_11.

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Rogers, Jonathan. "Monte Carlo Simulation of Dynamic Systems on GPU’s." In Numerical Computations with GPUs, 319–36. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06548-9_15.

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Macedo, Antonini Puppin, and Antonio C. P. Brasil. "A Coupled Monte Carlo/Explicit Euler Method for the Numerical Simulation of a Forest Fire Spreading Model." In Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, 333–45. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-2552-2_21.

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Van Rensburg, E. J. Janse, and N. Madras. "Monte Carlo Simulation of the Θ-Point in Lattice Trees." In Numerical Methods for Polymeric Systems, 141–57. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1704-6_9.

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Asenov, Asen. "Advanced Monte Carlo Techniques in the Simulation of CMOS Devices and Circuits." In Numerical Methods and Applications, 41–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18466-6_4.

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Тези доповідей конференцій з теми "Monte-Carlo numerical simulation"

Alekseev, N., A. Bolshakov, E. Mustafin, and P. Zenkevich. "Numerical code for Monte-Carlo simulation of ion storage." In Space charge dominated beam physics for heavy ion fusion. AIP, 1999. http://dx.doi.org/10.1063/1.59502.

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Ghosh, A., and K. K. Ghosh. "Monte Carlo Simulation of Excess Noise in Heterojunction Avalanche Photodetector." In 2007 International Conference on Numerical Simulation of Optoelectronic Devices. IEEE, 2007. http://dx.doi.org/10.1109/nusod.2007.4349014.

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Pacheco, K. A., and R. Guirardello. "Isotherm parameters for gas-solid adsorption using Monte Carlo simulation." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5044108.

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Hu, Z. X., Y. P. Wen, W. G. Zhao, H. P. Zhu, and S. L. Liu. "Numerical Simulation of Lightning Location Based on Monte Carlo Method." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5303752.

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Kivisaari, Pyry, Toufik Sadi, Jingrui Li, Jani Oksanen, Patrick Rinke, and Jukka Tulkki. "Bipolar Monte Carlo simulation of hot carriers in III-N LEDs." In 2015 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD). IEEE, 2015. http://dx.doi.org/10.1109/nusod.2015.7292797.

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Kivisaari, Pyry, Toufik Sadi, Jani Oksanen, and Jukka Tulkki. "Monte Carlo simulation of hot electron transport in III-N LEDs." In 14th International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD 2014). IEEE, 2014. http://dx.doi.org/10.1109/nusod.2014.6935336.

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Hobler, C., and S. Selberherr. "Efficient two-dimensional Monte Carlo simulation of ion implantation." In NASECODE V: Proceedings of the Fifth International Conference on the Numerical Analysis of Semiconductor Devices and Integrated Circuits. IEEE, 1987. http://dx.doi.org/10.1109/nascod.1987.721184.

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Kumar, Sunil, Zhixiong Guo, Janice Aber, and Bruce Garetz. "Experimental and Numerical Studies of Short Pulse Propagation in Model Systems." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-33903.

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In this paper experimental and numerical studies of the propagation of short-pulsed lasers through scattering and absorbing media are presented. Experimental results of a 60 ps pulse laser transmission in tissue phantoms are obtained and compared with Monte Carlo simulations. Good agreement between the Monte Carlo simulation and experimental measurement is found. Three models are developed for the simulation of short pulse transport. Benchmark comparisons among the Monte Carlo (MC), transient discrete ordinates method (TDOM) and transient radiation element method (TREM) are conducted.

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Barettin, Daniele, Morten Willatzen, Shima Kadkhodazadeh, Alessandro Pecchia, Matthias Auf der Maur, and E. S. Semenova. "A valence force field-Monte Carlo algorithm for quantum dot growth modeling." In 2017 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD). IEEE, 2017. http://dx.doi.org/10.1109/nusod.2017.8010019.

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Bridge, William J., and Adrian Korpel. "Monte Carlo simulation of strong acoustooptic interaction." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.mg3.

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In the conventional approach to strong acoustooptic interaction, the usual model is that of a rectangular sound column illuminated by a plane wave of light. By considering either the coupled modes of the unperturbed medium or the eigenmodes of the perturbed medium, a solution can then be found which predicts discrete plane waves of light as the diffracted orders. This model gives results in reasonable agreement with theory for configurations that approximate it. However, the physical reality of strongly interacting, arbitrary, diffracting sound and light fields is obviously very different from the simple, plane wave-sound column model. Qualitatively, any discrete orders predicted by the latter model can be explained in terms of multiple scattering in the physics based approach.1 Quantitatively, the solution must be framed in terms of Feynman diagrams and path integrals.2 A quantum mechanical interpretation of this formalism is one of photon transitions induced by the sound field. We report here on recent successful numerical experiments that simulate this quantum mechanical model by Monte Carlo methods using a classical computer.

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Звіти організацій з теми "Monte-Carlo numerical simulation"

Melby, Jeffrey, Thomas Massey, Fatima Diop, Himangshu Das, Norberto Nadal-Caraballo, Victor Gonzalez, Mary Bryant, et al. Coastal Texas Protection and Restoration Feasibility Study : Coastal Texas flood risk assessment : hydrodynamic response and beach morphology. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41051.

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The US Army Corps of Engineers, Galveston District, is executing the Coastal Texas Protection and Restoration Feasibility Study coastal storm risk management (CSRM) project for the region. The project is currently in the feasibility phase. The primary goal is to develop CSRM measures that maximize national net economic development benefits. This report documents the coastal storm water level and wave hazard, including sea level rise, for a variety of flood risk management alternatives. Four beach restoration alternatives for Galveston Island and Bolivar peninsula were evaluated. Suites of synthetic tropical and historical non-tropical storms were developed and modeled. The CSTORM coupled surge-and-wave modeling system was used to accurately characterize storm circulation, water level, and wave hazards using new model meshes developed from high-resolution land and sub-aqueous surveys for with- and without-project scenarios. Beach morphology stochastic response was modeled with a Monte Carlo life-cycle simulation approach using the CSHORE morphological evolution numerical model embedded in the StormSim stochastic modeling system. Morphological and hydrodynamic response were primarily characterized with probability distributions of the number of rehabilitations and overflow.

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Wagner, Anna, Chandler Engel, David Ho, Jeremy Giovando, Blaine Morriss, and Elias Deeb. Stage frequency analysis from snowmelt runoff near Utqiagvik, Alaska. Engineer Research and Development Center (U.S.), October 2023. http://dx.doi.org/10.21079/11681/47821.

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For the village of Utqiaġvik, located at the North Slope of Alaska, a stone-armored revetment along the coastline is proposed to reduce coastal erosion. The inner drainage capacity of the revetment must be sufficient to handle seasonal runoff from snowmelt. For this effort, we investigated the snowmelt runoff and the hydraulic impact at the watershed outlet using numerical snow and hydraulic modeling of the study area. We validated the snow model results by comparing simulated snow water equivalent (SWE) values to field measurements. Additionally, the snow model was validated using satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) snow-covered area (SCA) products and time-lapse camera imagery during snowmelt. Our results indicate that the simulated SWE and snowmelt dates agree closely with measured values. The timing of modeled runoff onset was less accurate due to natural processes that delay snowmelt runoff such as snow dams and refreeze. The effect of the uncertainty from both runoff timing and volume was addressed with a Monte Carlo simulation of stage-frequency curves for the lagoons that receive snowmelt runoff. These stage-frequency curves can be used directly in the design of outlet, drainage or discharge structures for the proposed revetment.

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Rojas-Bernal, Alejandro, and Mauricio Villamizar-Villegas. Pricing the exotic: Path-dependent American options with stochastic barriers. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/be.1156.

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We develop a novel pricing strategy that approximates the value of an American option with exotic features through a portfolio of European options with different maturities. Among our findings, we show that: (i) our model is numerically robust in pricing plain vanilla American options; (ii) the model matches observed bids and premiums of multidimensional options that integrate Ratchet, Asian, and Barrier characteristics; and (iii) our closed-form approximation allows for an analytical solution of the option’s greeks, which characterize the sensitivity to various risk factors. Finally, we highlight that our estimation requires less than 1% of the computational time compared to other standard methods, such as Monte Carlo simulations.

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, August 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maintaining required levels of accuracy. The growing availability of high-performance computing has improved this analysis by providing the ability to evaluate higher order numerical models. However, more complex models of the seismic response of various civil structures demand increasing amounts of computing power. In addition, computational cost greatly increases with numerous iterations to account for optimization and stochastic loading (e.g., Monte Carlo simulations or Incremental Dynamic Analysis). To address the large computational burden, simpler models are desired for seismic assessment with fragility analysis. Physics reinforced Machine Learning integrates physics knowledge (e.g., scientific principles, laws of physics) into the traditional machine learning architectures, offering physically bounded, interpretable models that require less data than traditional methods. This research introduces a PrML framework to develop fragility curves using the combination of neural networks of domain knowledge. The first aim involves clustering and selecting ground motions for nonlinear response analysis of archetype buildings, ensuring that selected ground motions will include as few ground motions as possible while still expressing all the key representative events the structure will probabilistically experience in its lifetime. The second aim constructs structural PrML metamodels to capture the nonlinear behavior of these buildings utilizing the nonlinear Equation of Motion (EOM). Embedding physical principles, like the general form of the EOM, into the learning process will inform the system to stay within known physical bounds, resulting in interpretable results, robust inferencing, and the capability of dealing with incomplete and scarce data. The third and final aim applies the metamodels to probabilistic seismic response prediction, fragility analysis, and seismic performance factor development. The efficiency and accuracy of this approach are evaluated against existing physics-based fragility analysis methods.

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BIFURCATION BUCKLING LOAD OF STEEL ANGLE WITH RANDOM CORROSION DAMAGE. The Hong Kong Institute of Steel Construction, June 2024. http://dx.doi.org/10.18057/ijasc.2024.20.2.7.

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This paper numerically studies the bifurcation buckling load of steel angle members affected by random corrosion pits. Six million Monte Carlo simulations are conducted, in which the effects of member length, section type, area loss ratio, and corrosion depth on the bifurcation buckling load of steel angles are considered. The key statistical characteristics of the reduction factors of buckling load for steel angles are analyzed. A probability-based relationship between the reduction factor of buckling load and the area loss ratio of steel angles is also proposed for the practical design. It is found that corrosion can potentially change the buckling mode of steel angle members from flexural buckling to torsional buckling. When the member length is small, the bifurcation buckling load of the steel angle is significantly affected by the corrosion depth. However, with the increase in member length, the effect of corrosion depth tends to decrease. The reduction factors of the buckling load of steel angles follow a normal distribution. A larger area loss ratio will result in a larger standard deviation of the reduction factors. For steel angles with the same area loss ratio, the mean values of the reduction factors of different section types are very close.

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