Literatura científica selecionada sobre o tema "Rare events simulation"
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Artigos de revistas sobre o assunto "Rare events simulation"
Becker, M., e P. L. Douillet. "Hierarchical Simulation For Rare Events". International Journal of Modelling and Simulation 17, n.º 2 (janeiro de 1997): 66–71. http://dx.doi.org/10.1080/02286203.1997.11760314.
Texto completo da fonteLagnoux, Agnès. "RARE EVENT SIMULATION". Probability in the Engineering and Informational Sciences 20, n.º 1 (12 de dezembro de 2005): 45–66. http://dx.doi.org/10.1017/s0269964806060025.
Texto completo da fonteKubatur, Shruthi S., e Mary L. Comer. "Simulation of Rare Events in Images". Electronic Imaging 2018, n.º 15 (28 de janeiro de 2018): 227–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.15.coimg-227.
Texto completo da fonteAsmussen, Søren, Reuven Y. Rubinstein e Chia-Li Wang. "Regenerative rare events simulation via likelihood ratios". Journal of Applied Probability 31, n.º 3 (setembro de 1994): 797–815. http://dx.doi.org/10.2307/3215157.
Texto completo da fonteAsmussen, Søren, Reuven Y. Rubinstein e Chia-Li Wang. "Regenerative rare events simulation via likelihood ratios". Journal of Applied Probability 31, n.º 03 (setembro de 1994): 797–815. http://dx.doi.org/10.1017/s0021900200045356.
Texto completo da fonteAsmussen, Søren, Klemens Binswanger, Bjarne Højgaard, Soren Asmussen e Bjarne Hojgaard. "Rare Events Simulation for Heavy-Tailed Distributions". Bernoulli 6, n.º 2 (abril de 2000): 303. http://dx.doi.org/10.2307/3318578.
Texto completo da fonteKabanov, A. A., e S. A. Dubovik. "Simulation of Rare Events in Stochastic Systems". Journal of Physics: Conference Series 2096, n.º 1 (1 de novembro de 2021): 012151. http://dx.doi.org/10.1088/1742-6596/2096/1/012151.
Texto completo da fonteTownsend, J. K., Z. Haraszti, J. A. Freebersyser e M. Devetsikiotis. "Simulation of rare events in communications networks". IEEE Communications Magazine 36, n.º 8 (1998): 36–41. http://dx.doi.org/10.1109/35.707815.
Texto completo da fonteChambers, W. G. "Simulation of rare events in Gaussian processes". Electronics Letters 29, n.º 15 (1993): 1384. http://dx.doi.org/10.1049/el:19930927.
Texto completo da fonteBréhier, Charles-Edouard, Maxime Gazeau, Ludovic Goudenège e Mathias Rousset. "Analysis and simulation of rare events for SPDEs". ESAIM: Proceedings and Surveys 48 (janeiro de 2015): 364–84. http://dx.doi.org/10.1051/proc/201448017.
Texto completo da fonteTeses / dissertações sobre o assunto "Rare events simulation"
Liu, Hong. "Rare events, heavy tails, and simulation". Diss., Connect to online resource, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3239435.
Texto completo da fonteBooth, Jonathan James. "New applications of boxed molecular dynamics : efficient simulation of rare events". Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/13101/.
Texto completo da fonteLiu, Gang. "Rare events simulation by shaking transformations : Non-intrusive resampler for dynamic programming". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLX043/document.
Texto completo da fonteThis thesis contains two parts: rare events simulation and non-intrusive stratified resampler for dynamic programming. The first part consists of quantifying statistics related to events which are unlikely to happen but which have serious consequences. We propose Markovian transformation on path spaces and combine them with the theories of interacting particle system and of Markov chain ergodicity to propose methods which apply very generally and have good performance. The second part consists of resolving dynamic programming problem numerically in a context where we only have historical observations of small size and we do not know the values of model parameters. We propose and analyze a new scheme with stratification and resampling techniques
DHAMODARAN, RAMYA. "EFFICIENT ANALYSIS OF RARE EVENTS ASSOCIATED WITH INDIVIDUAL BUFFERS IN A TANDEM JACKSON NETWORK". University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1099073321.
Texto completo da fonteFreitas, Rodrigo Moura 1989. "Molecular simulation = methods and applications = Simulações moleculares : métodos e aplicações". [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/278440.
Texto completo da fonteDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin
Made available in DSpace on 2018-08-23T00:50:21Z (GMT). No. of bitstreams: 1 Freitas_RodrigoMoura_M.pdf: 11496259 bytes, checksum: 41c29f22d80da01064cf7a3b9681b05f (MD5) Previous issue date: 2013
Resumo: Devido aos avanços conceptuais e técnicos feitos em física computacional e ciência dos materiais computacional nos estamos aptos a resolver problemas que eram inacessíveis a alguns anos atrás. Nessa dissertação estudamos a evolução de alguma destas técnicas, apresentando a teoria e técnicas de simulação computacional para estudar transições de fase de primeira ordem com ênfase nas técnicas mais avançadas de calculo de energia livre (Reversible Scaling) e métodos de simulação de eventos raros (Forward Flux Sampling) usando a técnica de simulação atomística da Dinâmica Molecular. A evolução e melhora da e ciência destas técnicas e apresentada junto com aplicações a sistemas simples que permitem solução exata e também ao caso mais complexo da transição de fase Martenstica. Também apresentamos a aplicação de métodos numéricos no estudo do modelo de Pauling para o gelo. Nos desenvolvemos e implementamos um novo algoritmo para a criação e ciente de estruturas de gelo desordenadas. Este algoritmo de geração de cristais de gelo nos permitiu criar células de gelo Ih de tamanhos que não eram possíveis antes. Usando este algoritmo abordamos o problema de efeitos de tamanho finito não estudados anteriormente
Abstract: Due to the conceptual and technical advances being made in computational physics and computational materials science we have been able to tackle problems that were inaccessible a few years ago. In this dissertation we study the evolution of some of these techniques, presenting the theory and simulation methods to study _rst order phase transitions with emphasis on state-of-the-art free-energy calculation (Reversible Scaling) and rare event (Forward Flux Sampling) methods using the atomistic simulation technique of Molecular Dynamics. The evolution and efficiency improvement of these techniques is presented together with applications to simple systems that allow exact solution as well as the more the complex case of Martensitic phase transitions. We also present the application of numerical methods to study Pauling\'s model of ice. We have developed and implemented a new algorithm for efficient generation of disordered ice structures. This ice generator algorithm allows us to create ice Ih cells of sizes not reported before. Using this algorithm we address finite size effects not studied before
Mestrado
Física
Mestre em Física
Hewett, Angela Dawn. "Expecting the unexpected : to what extent does simulation help healthcare professionals prepare for rare, critical events during childbearing?" Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/15431/.
Texto completo da fonteRai, Ajit. "Estimation de la disponibilité par simulation, pour des systèmes incluant des contraintes logistiques". Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S105/document.
Texto completo da fonteRAM (Reliability, Availability and Maintainability) analysis forms an integral part in estimation of Life Cycle Costs (LCC) of passenger rail systems. These systems are highly reliable and include complex logistics. Standard Monte-Carlo simulations are rendered useless in efficient estimation of RAM metrics due to the issue of rare events. Systems failures of these complex passenger rail systems can include rare events and thus need efficient simulation techniques. Importance Sampling (IS) are an advanced class of variance reduction techniques that can overcome the limitations of standard simulations. IS techniques can provide acceleration of simulations, meaning, less variance in estimation of RAM metrics in same computational budget as a standard simulation. However, IS includes changing the probability laws (change of measure) that drive the mathematical models of the systems during simulations and the optimal IS change of measure is usually unknown, even though theroretically there exist a perfect one (zero-variance IS change of measure). In this thesis, we focus on the use of IS techniques and its application to estimate two RAM metrics : reliability (for static networks) and steady state availability (for dynamic systems). The thesis focuses on finding and/or approximating the optimal IS change of measure to efficiently estimate RAM metrics in rare events context. The contribution of the thesis is broadly divided into two main axis : first, we propose an adaptation of the approximate zero-variance IS method to estimate reliability of static networks and show the application on real passenger rail systems ; second, we propose a multi-level Cross-Entropy optimization scheme that can be used during pre-simulation to obtain CE optimized IS rates of Markovian Stochastic Petri Nets (SPNs) transitions and use them in main simulations to estimate steady state unavailability of highly reliably Markovian systems with complex logistics involved. Results from the methods show huge variance reduction and gain compared to MC simulations
Picciani, Massimiliano. "Rare events in many-body systems : reactive paths and reaction constants for structural transitions". Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00706510.
Texto completo da fonteSilva, lopes Laura. "Méthodes numériques pour la simulation d'évènements rares en dynamique moléculaire". Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1045.
Texto completo da fonteIn stochastic dynamical systems, such as those encountered in molecular dynamics, rare events naturally appear as events due to some low probability stochastic fluctuations. Examples of rare events in our everyday life includes earthquakes and major floods. In chemistry, protein folding, ligandunbinding from a protein cavity and opening or closing of channels in cell membranes are examples of rare events. Simulation of rare events has been an important field of research in biophysics over the past thirty years.The events of interest in molecular dynamics generally involve transitions between metastable states, which are regions of the phase space where the system tends to stay trapped. These transitions are rare, making the use of a naive, direct Monte Carlo method computationally impracticable. To dealwith this difficulty, sampling methods have been developed to efficiently simulate rare events. Among them are splitting methods, that consists in dividing the rare event of interest into successive nested more likely events.Adaptive Multilevel Splitting (AMS) is a splitting method in which the positions of the intermediate interfaces, used to split reactive trajectories, are adapted on the fly. The surfaces are defined suchthat the probability of transition between them is constant, which minimizes the variance of the rare event probability estimator. AMS is a robust method that requires a small quantity of user defined parameters, and is therefore easy to use.This thesis focuses on the application of the adaptive multilevel splitting method to molecular dynamics. Two kinds of systems are studied. The first one contains simple models that allowed us to improve the way AMS is used. The second one contains more realistic and challenging systems, where AMS isused to get better understanding of the molecular mechanisms. Hence, the contributions of this thesis include both methodological and numerical results.We first validate the AMS method by applying it to the paradigmatic alanine dipeptide conformational change. We then propose a new technique combining AMS and importance sampling to efficiently sample the initial conditions ensemble when using AMS to obtain the transition time. This is validatedon a simple one dimensional problem, and our results show its potential for applications in complex multidimensional systems. A new way to identify reaction mechanisms is also proposed in this thesis.It consists in performing clustering techniques over the reactive trajectories ensemble generated by the AMS method.The implementation of the AMS method for NAMD has been improved during this thesis work. In particular, this manuscript includes a tutorial on how to use AMS on NAMD. The use of the AMS method allowed us to study two complex molecular systems. The first consists in the analysis of the influence of the water model (TIP3P and TIP4P/2005) on the β -cyclodextrin and ligand unbinding process. In the second, we apply the AMS method to sample unbinding trajectories of a ligand from the N-terminal domain of the Hsp90 protein
Saggadi, Samira. "Simulation d'évènements rares par Monte Carlo dans les réseaux hautement fiables". Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S055.
Texto completo da fonteNetwork reliability determination, is an NP-hard problem. For instance, in telecommunications, it is desired to evaluate the probability that a selected group of nodes communicate or not. In this case, a set of disconnected nodes can lead to critical financials security consequences. A precise estimation of the reliability is, therefore, needed. In this work, we are interested in the study and the calculation of the reliability of highly reliable networks. In this case the unreliability is very small, which makes the standard Monte Carlo approach useless, because it requires a large number of iterations. For a good estimation of system reliability with minimum cost, we have developed new simulation techniques based on variance reduction using importance sampling
Livros sobre o assunto "Rare events simulation"
Shi, Yixi. Rare Events in Stochastic Systems: Modeling, Simulation Design and Algorithm Analysis. [New York, N.Y.?]: [publisher not identified], 2013.
Encontre o texto completo da fonteBucklew, James Antonio. Introduction to Rare Event Simulation. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4078-3.
Texto completo da fonteBucklew, James Antonio. Introduction to Rare Event Simulation. New York, NY: Springer New York, 2004.
Encontre o texto completo da fonteRubino, Gerardo, e Bruno Tuffin, eds. Rare Event Simulation using Monte Carlo Methods. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745403.
Texto completo da fonte1955-, Rubino Gerardo, e Tuffin Bruno, eds. Rare event simulation using Monte Carlo methods. Hoboken, N.J: Wiley, 2009.
Encontre o texto completo da fonteLamers, Eugen. Contributions to Simulation Speed-Up: Rare Event Simulation and Short-Term Dynamic Simulation for Mobile Network Planning. Wiesbaden: Vieweg+Teubner / GWV Fachverlage GmbH, Wiesbaden, 2008.
Encontre o texto completo da fonteAllen, Michael P., e Dominic J. Tildesley. Rare event simulation. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803195.003.0010.
Texto completo da fonte(Editor), Bruce J. Berne, Giovanni Cicotti (Editor) e David F. Coker (Editor), eds. Classical and Quantum Dynamics in Condensed Phase Simulations: Proceedings of the International School of Physics "Computer Simulation of Rare Events and ... Classical and Quantum Condensed-Phase syste. World Scientific Publishing Company, 1998.
Encontre o texto completo da fonteBucklew, James A. Introduction to Rare Event Simulation. Springer, 2004.
Encontre o texto completo da fonteAn introduction to rare event simulation. New York: Springer, 2003.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Rare events simulation"
Ciccotti, G. "Molecular Dynamics Simulations of Nonequilibrium Phenomena and Rare Dynamical Events". In Computer Simulation in Materials Science, 119–37. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3546-7_6.
Texto completo da fonteRubino, Gerardo. "Network Reliability, Performability Metrics, Rare Events and Standard Monte Carlo". In Advances in Modeling and Simulation, 401–20. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10193-9_20.
Texto completo da fonteWainrib, Gilles. "Some Numerical Methods for Rare Events Simulation and Analysis". In Lecture Notes in Mathematics, 73–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32157-3_4.
Texto completo da fonteAsmussen, Søren. "Large Deviations in Rare Events Simulation: Examples, Counterexamples and Alternatives". In Monte Carlo and Quasi-Monte Carlo Methods 2000, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56046-0_1.
Texto completo da fontePelikan, Martin, Jiri Ocenasek, Simon Trebst, Matthias Troyer e Fabien Alet. "Computational Complexity and Simulation of Rare Events of Ising Spin Glasses". In Genetic and Evolutionary Computation – GECCO 2004, 36–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24855-2_4.
Texto completo da fonteCarney, Meagan, Holger Kantz e Matthew Nicol. "Analysis and Simulation of Extremes and Rare Events in Complex Systems". In Advances in Dynamics, Optimization and Computation, 151–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51264-4_7.
Texto completo da fonteD’Argenio, Pedro R., Carlos E. Budde, Matias David Lee, Raúl E. Monti, Leonardo Rodríguez e Nicolás Wolovick. "The Road from Stochastic Automata to the Simulation of Rare Events". In ModelEd, TestEd, TrustEd, 276–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68270-9_14.
Texto completo da fontevan Moorsel, Aad P. A., Boudewijn R. Haverkort e Ignas G. Niemegeers. "Fault Injection Simulation: A Variance Reduction Technique for Systems with Rare Events". In Dependable Computing for Critical Applications 2, 115–34. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-9198-9_6.
Texto completo da fonteZimmermann, Armin. "Extended Reward Measures in the Simulation of Embedded Systems With Rare Events". In Embedded Systems – Modeling, Technology, and Applications, 43–52. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-4933-1_5.
Texto completo da fontePuch, Stefan, Martin Fränzle e Sebastian Gerwinn. "Quantitative Risk Assessment of Safety-Critical Systems via Guided Simulation for Rare Events". In Leveraging Applications of Formal Methods, Verification and Validation. Verification, 305–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03421-4_20.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Rare events simulation"
Foo, Jasmine, e Kevin Leder. "Rare events in cancer recurrence timing". In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465239.
Texto completo da fonteKim, Youngjun, Yonatan Gur e Mykel J. Kochenderfer. "Heuristics for planning with rare catastrophic events". In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8248024.
Texto completo da fonteGordon, Steven, e David Garbin. "Of dual-core networks during rare events". In 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6148017.
Texto completo da fonteFresnedo, R. D. "Quick simulation of rare events in networks". In the 21st conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/76738.76805.
Texto completo da fonteWang, Xingyu, e Chang-Han Rhee. "Rare-Event Simulation for Multiple Jump Events in Heavy-Tailed Lévy Processes with Infinite Activities". In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9383865.
Texto completo da fonteEstecahandy, Maider, Laurent Bordes, Stephane Collas e Christian Paroissin. "Acceleration methods for Monte Carlo simulation of rare events". In 2015 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2015. http://dx.doi.org/10.1109/rams.2015.7105098.
Texto completo da fonteNavarro, Jose M., G. Hugo A. Parada e Juan C. Duenas. "System Failure Prediction through Rare-Events Elastic-Net Logistic Regression". In 2014 2nd International Conference on Artificial Intelligence, Modelling & Simulation (AIMS). IEEE, 2014. http://dx.doi.org/10.1109/aims.2014.19.
Texto completo da fonteGuo, Yabing, Wenbing Chang e Shenghan Zhou. "The research and simulation about rare events based on MCMC". In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162009.
Texto completo da fonte"Resampling techniques for rare events prediction using data-driven and hydrological models". In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.zeinolabedini.
Texto completo da fontePang, Yanbo, Kota Tsubouchi, Takahiro Yabe e Yoshihide Sekimoto. "Intercity Simulation of Human Mobility at Rare Events via Reinforcement Learning". In SIGSPATIAL '20: 28th International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397536.3422244.
Texto completo da fonteRelatórios de organizações sobre o assunto "Rare events simulation"
Washio, Takashi. Model Learning for Probabilistic Simulation on Rare Events and Scenarios. Fort Belvoir, VA: Defense Technical Information Center, março de 2015. http://dx.doi.org/10.21236/ada616937.
Texto completo da fonteKollman, Craig. Rare event simulation in radiation transport. Office of Scientific and Technical Information (OSTI), outubro de 1993. http://dx.doi.org/10.2172/10172053.
Texto completo da fonteSarupria, Sapna, Steven Hall e Ryan DeFever. Sampling Rare Events In Aqueous Systems Using Molecular Simulations. Office of Scientific and Technical Information (OSTI), junho de 2024. http://dx.doi.org/10.2172/2376138.
Texto completo da fonteShortle, John F. New Approaches for Rare-Event Simulation and Decision Making. Office of Scientific and Technical Information (OSTI), setembro de 2013. http://dx.doi.org/10.2172/1128906.
Texto completo da fonteMartin, S., Larry Daggett, Morgan Johnston, Chris Hewlett, Kiara Pazan, Mario Sanchez, Dennis Webb, Mary Allison e George Burkley. Houston Ship Channel Expansion Improvement Project – Navigation Channel Improvement Study : ship simulation results. Coastal and Hydraulics Laboratory (U.S.), novembro de 2021. http://dx.doi.org/10.21079/11681/42342.
Texto completo da fonteDean, Thomas, e aul Dupuis. Splitting for Rare Event Simulation: A Large Deviations Approach to Design and Analysis. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 2007. http://dx.doi.org/10.21236/ada476257.
Texto completo da fonteWallace, Adam. Towards The Development of Rare Event Simulation Methods For Improved Mechanistic Understanding of Mineral Surface Reactivity. Office of Scientific and Technical Information (OSTI), setembro de 2024. http://dx.doi.org/10.2172/2447356.
Texto completo da fonteKushner, Harold, e Paul Dupuis. Stochastic Control and Numerical Methods with Applications to Communications. Game Theoretic/Subsolution to Importance Sampling for Rare Event Simulation. Fort Belvoir, VA: Defense Technical Information Center, novembro de 2008. http://dx.doi.org/10.21236/ada499989.
Texto completo da fonteChazel, Simon, Sophie Bernard e Hassan Benchekroun. Energy transition under mineral constraints and recycling: A low-carbon supply peak. CIRANO, maio de 2023. http://dx.doi.org/10.54932/ezhr6690.
Texto completo da fonteKotlikoff, Laurence J., Guillermo Lagarda e Gabriel Marin. A Personalized VAT with Capital Transfers: A Reform to Protect Low-Income Households in Mexico. Inter-American Development Bank, julho de 2023. http://dx.doi.org/10.18235/0005028.
Texto completo da fonte