Literatura científica selecionada sobre o tema "Rare event probability"
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Artigos de revistas sobre o assunto "Rare event probability"
Sinha, Ashoke Kumar, e Laurens de Haan. "Estimating the probability of a rare event". Annals of Statistics 27, n.º 2 (abril de 1999): 732–59. http://dx.doi.org/10.1214/aos/1018031214.
Texto completo da fonteAbbot, Dorian S., Robert J. Webber, Sam Hadden, Darryl Seligman e Jonathan Weare. "Rare Event Sampling Improves Mercury Instability Statistics". Astrophysical Journal 923, n.º 2 (1 de dezembro de 2021): 236. http://dx.doi.org/10.3847/1538-4357/ac2fa8.
Texto completo da fonteHo, Yu-Chi, e Michael E. Larson. "Ordinal optimization approach to rare event probability problems". Discrete Event Dynamic Systems: Theory and Applications 5, n.º 2-3 (abril de 1995): 281–301. http://dx.doi.org/10.1007/bf01439043.
Texto completo da fonteChan, Joshua C. C., e Dirk P. Kroese. "Rare-event probability estimation with conditional Monte Carlo". Annals of Operations Research 189, n.º 1 (24 de março de 2009): 43–61. http://dx.doi.org/10.1007/s10479-009-0539-y.
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 fontePicard, Richard R. "Introduction to Rare Event Simulation". Journal of the American Statistical Association 100, n.º 471 (setembro de 2005): 1091–92. http://dx.doi.org/10.1198/jasa.2005.s32.
Texto completo da fontePienaar, Elsje. "Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems". Biomedical Engineering and Computational Biology 9 (janeiro de 2018): 117959721879025. http://dx.doi.org/10.1177/1179597218790253.
Texto completo da fonteBalesdent, Mathieu, Jérôme Morio e Julien Marzat. "Recommendations for the tuning of rare event probability estimators". Reliability Engineering & System Safety 133 (janeiro de 2015): 68–78. http://dx.doi.org/10.1016/j.ress.2014.09.001.
Texto completo da fonteWiorkowski, John. "Finding the probability of a rare real world event". Mathematical Gazette 103, n.º 557 (6 de junho de 2019): 240–47. http://dx.doi.org/10.1017/mag.2019.55.
Texto completo da fonteDobson, Ian, Benjamin A. Carreras e David E. Newman. "How Many Occurrences of Rare Blackout Events Are Needed to Estimate Event Probability?" IEEE Transactions on Power Systems 28, n.º 3 (agosto de 2013): 3509–10. http://dx.doi.org/10.1109/tpwrs.2013.2242700.
Texto completo da fonteTeses / dissertações sobre o assunto "Rare event probability"
Drozdenko, Myroslav. "Weak Convergence of First-Rare-Event Times for Semi-Markov Processes". Doctoral thesis, Västerås : Mälardalen University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-394.
Texto completo da fonteRazaaly, Nassim. "Rare Event Estimation and Robust Optimization Methods with Application to ORC Turbine Cascade". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLX027.
Texto completo da fonteThis thesis aims to formulate innovative Uncertainty Quantification (UQ) methods in both Robust Optimization (RO) and Reliability-Based Design Optimization (RBDO) problems. The targeted application is the optimization of supersonic turbines used in Organic Rankine Cycle (ORC) power systems.Typical energy sources for ORC power systems feature variable heat load and turbine inlet/outlet thermodynamic conditions. The use of organic compounds with a heavy molecular weight typically leads to supersonic turbine configurations featuring supersonic flows and shocks, which grow in relevance in the aforementioned off-design conditions; these features also depend strongly on the local blade shape, which can be influenced by the geometric tolerances of the blade manufacturing. A consensus exists about the necessity to include these uncertainties in the design process, so requiring fast UQ methods and a comprehensive tool for performing shape optimization efficiently.This work is decomposed in two main parts. The first one addresses the problem of rare events estimation, proposing two original methods for failure probability (metaAL-OIS and eAK-MCS) and one for quantile computation (QeAK-MCS). The three methods rely on surrogate-based (Kriging) adaptive strategies, aiming at refining the so-called Limit-State Surface (LSS) directly, unlike Subset Simulation (SS) derived methods. Indeed, the latter consider intermediate threshold associated with intermediate LSSs to be refined. This direct refinement property is of crucial importance since it enables the adaptability of the developed methods for RBDO algorithms. Note that the proposed algorithms are not subject to restrictive assumptions on the LSS (unlike the well-known FORM/SORM), such as the number of failure modes, however need to be formulated in the Standard Space. The eAK-MCS and QeAK-MCS methods are derived from the AK-MCS method and inherit a parallel adaptive sampling based on weighed K-Means. MetaAL-OIS features a more elaborate sequential refinement strategy based on MCMC samples drawn from a quasi-optimal ISD. It additionally proposes the construction of a Gaussian mixture ISD, permitting the accurate estimation of small failure probabilities when a large number of evaluations (several millions) is tractable, as an alternative to SS. The three methods are shown to perform very well for 2D to 8D analytical examples popular in structural reliability literature, some featuring several failure modes, all subject to very small failure probability/quantile level. Accurate estimations are performed in the cases considered using a reasonable number of calls to the performance function.The second part of this work tackles original Robust Optimization (RO) methods applied to the Shape Design of a supersonic ORC Turbine cascade. A comprehensive Uncertainty Quantification (UQ) analysis accounting for operational, fluid parameters and geometric (aleatoric) uncertainties is illustrated, permitting to provide a general overview over the impact of multiple effects and constitutes a preliminary study necessary for RO. Then, several mono-objective RO formulations under a probabilistic constraint are considered in this work, including the minimization of the mean or a high quantile of the Objective Function. A critical assessment of the (Robust) Optimal designs is finally investigated
Sinks, Shuxian. "Response Adaptive Design using Auxiliary and Primary Outcomes". VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/572.
Texto completo da fonteChabridon, Vincent. "Analyse de sensibilité fiabiliste avec prise en compte d'incertitudes sur le modèle probabiliste - Application aux systèmes aérospatiaux". Thesis, Université Clermont Auvergne (2017-2020), 2018. http://www.theses.fr/2018CLFAC054/document.
Texto completo da fonteAerospace systems are complex engineering systems for which reliability has to be guaranteed at an early design phase, especially regarding the potential tremendous damage and costs that could be induced by any failure. Moreover, the management of various sources of uncertainties, either impacting the behavior of systems (“aleatory” uncertainty due to natural variability of physical phenomena) and/or their modeling and simulation (“epistemic” uncertainty due to lack of knowledge and modeling choices) is a cornerstone for reliability assessment of those systems. Thus, uncertainty quantification and its underlying methodology consists in several phases. Firstly, one needs to model and propagate uncertainties through the computer model which is considered as a “black-box”. Secondly, a relevant quantity of interest regarding the goal of the study, e.g., a failure probability here, has to be estimated. For highly-safe systems, the failure probability which is sought is very low and may be costly-to-estimate. Thirdly, a sensitivity analysis of the quantity of interest can be set up in order to better identify and rank the influential sources of uncertainties in input. Therefore, the probabilistic modeling of input variables (epistemic uncertainty) might strongly influence the value of the failure probability estimate obtained during the reliability analysis. A deeper investigation about the robustness of the probability estimate regarding such a type of uncertainty has to be conducted. This thesis addresses the problem of taking probabilistic modeling uncertainty of the stochastic inputs into account. Within the probabilistic framework, a “bi-level” input uncertainty has to be modeled and propagated all along the different steps of the uncertainty quantification methodology. In this thesis, the uncertainties are modeled within a Bayesian framework in which the lack of knowledge about the distribution parameters is characterized by the choice of a prior probability density function. During a first phase, after the propagation of the bi-level input uncertainty, the predictive failure probability is estimated and used as the current reliability measure instead of the standard failure probability. Then, during a second phase, a local reliability-oriented sensitivity analysis based on the use of score functions is achieved to study the impact of hyper-parameterization of the prior on the predictive failure probability estimate. Finally, in a last step, a global reliability-oriented sensitivity analysis based on Sobol indices on the indicator function adapted to the bi-level input uncertainty is proposed. All the proposed methodologies are tested and challenged on a representative industrial aerospace test-case simulating the fallout of an expendable space launcher
Krauth, Timothé. "Modèle génératif profond pour l'estimation de probabilité de collision en vol". Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0018.
Texto completo da fonteIt is essential to calculate the probability of aircraft collisions to optimise air traffic while maintaining high safety standards. This need became more pronounced in the 1960s with the increase in transatlantic commercial air traffic. Initially, analytical models such as those of Reich and Anderson-Hsu were benchmarks for assessing in-flight collision risks, but they proved to be less suited for the complex airspace around airports.Data-driven methods, especially Monte Carlo simulations, have become a promising alternative for collision risk assessment. They offer significant flexibility through simplified assumptions, making them adaptable to various contexts. However, traditional Monte Carlo simulations are inefficient for estimating rare event probabilities, requiring a large number of aircraft trajectories and substantial computational resources. This thesis proposes a collision risk model based on Monte Carlo simulations, using a trajectory generation model to overcome these limitations associated with rare events. These generative methods faithfully reproduce observed trajectory distributions while incorporating uncertainties from external factors. Three main research areas are defined: (i) developing a trajectory generation method, (ii) constructing a Monte Carlo-based collision risk model using synthetic trajectories, and (iii) improving the interpretability of collision risk estimates.Generating synthetic samples involves estimating the distribution of observed data to ensure identical distribution in new samples. This is particularly important for aircraft trajectories, where the model must reflect uncertainty sources causing deviations from standard trajectories. We initially use traditional statistical learning methods to estimate complex two-dimensional aircraft trajectories. Despite reducing the problem's dimensionality, conventional methods struggle with high-dimensional distribution estimation. We then explore the use of variational autoencoders for more refined probability density estimation. Suitably adapted for multivariate time-series applications, variational autoencoders prove effective for estimating the distribution of complex aircraft trajectories.Using the developed generation method, we estimate the risk of loss of separation induced by the departure and approach procedures of Paris-Orly Airport using Monte Carlo simulations. The use of a trajectory generation method proves promising, allowing the creation of the equivalent of 20 years of air traffic trajectories from only two months of observations. However, this direct method has limitations for estimating extremely low collision probabilities, requiring the use of one variational autoencoder per flight procedure considered in the studied scenario. The processes of trajectory generation and collision risk evaluation are distinctly separated. Consequently, the inherent constraints of classical Monte Carlo methods are not truly overcome but merely postponed by the production of a set of arbitrarily large trajectories.The thesis's final work unifies the frameworks of variational autoencoders and uncertainty quantification. It demonstrates how variational autoencoders can build suitable input distributions for uncertainty quantification algorithms, enhancing the reliability of Monte Carlo simulations through subset simulation and the explainability of mid-air collision probability estimation through sensitivity analysis. More broadly, we show that the variational autoencoder represents a promising tool to be associated with uncertainty quantification problems
Jacquemart, Damien. "Contributions aux méthodes de branchement multi-niveaux pour les évènements rares, et applications au trafic aérien". Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S186/document.
Texto completo da fonteThe thesis deals with the design and mathematical analysis of reliable and accurate Monte Carlo methods in order to estimate the (very small) probability that a Markov process reaches a critical region of the state space before a deterministic final time. The underlying idea behind the multilevel splitting methods studied here is to design an embedded sequence of intermediate more and more critical regions, in such a way that reaching an intermediate region, given that the previous intermediate region has already been reached, is not so rare. In practice, trajectories are propagated, selected and replicated as soon as the next intermediate region is reached, and it is easy to accurately estimate the transition probability between two successive intermediate regions. The bias due to time discretization of the Markov process trajectories is corrected using perturbed intermediate regions as proposed by Gobet and Menozzi. An adaptive version would consist in the automatic design of the intermediate regions, using empirical quantiles. However, it is often difficult if not impossible to remember where (in which state) and when (at which time instant) did each successful trajectory reach the empirically defined intermediate region. The contribution of the thesis consists in using a first population of pilot trajectories to define the next threshold, in using a second population of trajectories to estimate the probability of exceeding this empirically defined threshold, and in iterating these two steps (definition of the next threshold, and evaluation of the transition probability) until the critical region is reached. The convergence of this adaptive two-step algorithm is studied in the asymptotic framework of a large number of trajectories. Ideally, the intermediate regions should be defined in terms of the spatial and temporal variables jointly (for example, as the set of states and times for which a scalar function of the state exceeds a time-dependent threshold). The alternate point of view proposed in the thesis is to keep intermediate regions as simple as possible, defined in terms of the spatial variable only, and to make sure that trajectories that manage to exceed a threshold at an early time instant are more replicated than trajectories that exceed the same threshold at a later time instant. The resulting algorithm combines importance sampling and multilevel splitting. Its preformance is evaluated in the asymptotic framework of a large number of trajectories, and in particular a central limit theorem is obtained for the relative approximation error
Yu, Xiaomin. "Simulation Study of Sequential Probability Ratio Test (SPRT) in Monitoring an Event Rate". University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1244562576.
Texto completo da fonteDHAMODARAN, 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 fonteEstecahandy, Maïder. "Méthodes accélérées de Monte-Carlo pour la simulation d'événements rares. Applications aux Réseaux de Petri". Thesis, Pau, 2016. http://www.theses.fr/2016PAUU3008/document.
Texto completo da fonteThe dependability analysis of safety instrumented systems is an important industrial concern. To be able to carry out such safety studies, TOTAL develops since the eighties the dependability software GRIF. To take into account the increasing complexity of the operating context of its safety equipment, TOTAL is more frequently led to use the engine MOCA-RP of the GRIF Simulation package. Indeed, MOCA-RP allows to estimate quantities associated with complex aging systems modeled in Petri nets thanks to the standard Monte Carlo (MC) simulation. Nevertheless, deriving accurate estimators, such as the system unavailability, on very reliable systems involves rare event simulation, which requires very long computing times with MC. In order to address this issue, the common fast Monte Carlo methods do not seem to be appropriate. Many of them are originally defined to improve only the estimate of the unreliability and/or well-suited for Markovian processes. Therefore, the work accomplished in this thesis pertains to the development of acceleration methods adapted to the problematic of performing safety studies modeled in Petri nets and estimating in particular the unavailability. More specifically, we propose the Extension of the "Méthode de Conditionnement Temporel" to accelerate the individual failure of the components, and we introduce the Dissociation Method as well as the Truncated Fixed Effort Method to increase the occurrence of their simultaneous failures. Then, we combine the first technique with the two other ones, and we also associate them with the Randomized Quasi-Monte Carlo method. Through different sensitivities studies and benchmark experiments, we assess the performance of the acceleration methods and observe a significant improvement of the results compared with MC. Furthermore, we discuss the choice of the confidence interval method to be used when considering rare event simulation, which is an unfamiliar topic in the field of dependability. Last, an application to an industrial case permits the illustration of the potential of our solution methodology
Mattrand, Cécile. "Approche probabiliste de la tolérance aux dommages". Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2011. http://tel.archives-ouvertes.fr/tel-00738947.
Texto completo da fonteLivros sobre o assunto "Rare event probability"
1955-, Rubino Gerardo, e Tuffin Bruno, eds. Rare event simulation using Monte Carlo methods. Hoboken, N.J: Wiley, 2009.
Encontre o texto completo da fonteJürg, Hüsler, Reiss Rolf-Dieter e SpringerLink (Online service), eds. Laws of Small Numbers: Extremes and Rare Events. Basel: Springer Basel AG, 2011.
Encontre o texto completo da fonteKalashnikov, Vladimir Vi͡acheslavovich. Geometric sums, bounds for rare events with applications: Risk analysis, reliability, queueing. Dordrecht: Kluwer Academic, 1997.
Encontre o texto completo da fonteFalk, Michael. Laws of small numbers: Extremes and rare events. 2a ed. Basel: Birkhauser Verlag, 2004.
Encontre o texto completo da fonteFalk, Michael. Laws of small numbers: Extremes and rare events. Basel: Birkhäuser Verlag, 1994.
Encontre o texto completo da fonteKalashnikov, Vladimir. Geometric Sums: Bounds for Rare Events with Applications: Risk Analysis, Reliability, Queueing. Dordrecht: Springer Netherlands, 1997.
Encontre o texto completo da fonteSchneider, Jörg, e Ton Vrouwenvelder. Introduction to safety and reliability of structures. 3a ed. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 1997. http://dx.doi.org/10.2749/sed005.
Texto completo da fonte1969-, Lee-Treweek Geraldine, e Linkogle Stephanie, eds. Danger in the field: Risk and ethics in social research. London: Routledge, 2000.
Encontre o texto completo da fonteClark, James S., Dave Bell, Michael Dietze, Michelle Hersh, Ines Ibanez, Shannon LaDeau, Sean McMahon et al. Assessing the probability of rare climate events. Editado por Anthony O'Hagan e Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.16.
Texto completo da fonteHüsler, Jürg, Rolf-Dieter Reiss e Michael Falk. Laws of Small Numbers: Extremes and Rare Events. Birkhauser Verlag, 2013.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Rare event probability"
Caron, Virgile. "Importance Sampling for Multi-Constraints Rare Event Probability". In Springer Proceedings in Mathematics & Statistics, 119–28. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2104-1_11.
Texto completo da fonteVořechovský, Miroslav. "Active Learning for Efficient Rare Event Probability Estimation and Sensitivity Analyses in Highly Nonlinear Systems". In Lecture Notes in Civil Engineering, 324–33. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60271-9_30.
Texto completo da fonteKalashnikov, Vladimir. "Ruin Probability". In Geometric Sums: Bounds for Rare Events with Applications, 171–200. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-1693-2_6.
Texto completo da fonteKalashnikov, Vladimir. "Miscellaneous Probability Topics". In Geometric Sums: Bounds for Rare Events with Applications, 30–73. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-1693-2_2.
Texto completo da fonteHüsler, Jürg. "Extreme Values and Rare Events of Non-Stationary Random Sequences". In Dependence in Probability and Statistics, 439–56. Boston, MA: Birkhäuser Boston, 1986. http://dx.doi.org/10.1007/978-1-4615-8162-8_21.
Texto completo da fonteLongpré, Luc, e Vladik Kreinovich. "How to Describe Hypothetic Truly Rare Events (With Probability 0)". In Uncertainty, Constraints, and Decision Making, 211–15. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36394-8_35.
Texto completo da fonteJoy, Christy, e Marria C. Cyriac. "Phytochemicals as Potential Drug Candidates for SARS Cov-2: An RDRp Based In-Silico Drug Designing". In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 58–69. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_7.
Texto completo da fonteMorio, J., e M. Balesdent. "Introduction to rare event probability estimation". In Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems, 1–2. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-08-100091-5.00001-0.
Texto completo da fonteMorio, J., e M. Balesdent. "Basics of probability and statistics". In Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems, 5–32. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-08-100091-5.00002-2.
Texto completo da fonteMorio, J., D. Jacquemart e M. Balesdent. "Estimation of conflict probability between aircraft". In Estimation of Rare Event Probabilities in Complex Aerospace and Other Systems, 183–88. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-08-100091-5.00013-7.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Rare event probability"
Botev, Zdravko I., e Ad Ridder. "An M-estimator for rare-event probability estimation". In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822103.
Texto completo da fonteShah, Rohan, Christian Hirsch, Dirk P. Kroese e Volker Schmidt. "Rare event probability estimation for connectivity of large random graphs". In 2014 Winter Simulation Conference - (WSC 2014). IEEE, 2014. http://dx.doi.org/10.1109/wsc.2014.7019916.
Texto completo da fonteQiu, Yue, Hong Zhou e Yue-qin Wu. "An importance sampling method with applications to rare event probability". In 2007 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2007. http://dx.doi.org/10.1109/gsis.2007.4443499.
Texto completo da fonteElsheikh, A. H., S. Oladyshkin, W. Nowak e M. Christie. "Estimating the Probability of CO2 Leakage Using Rare Event Simulation". In ECMOR XIV - 14th European Conference on the Mathematics of Oil Recovery. Netherlands: EAGE Publications BV, 2014. http://dx.doi.org/10.3997/2214-4609.20141876.
Texto completo da fontede Boer, Pieter-Tjerk, Pierre L'Ecuyer, Gerardo Rubino e Bruno Tuffin. "Estimating the probability of a rare event over a finite time horizon". In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419629.
Texto completo da fonteCavaluzzi, Jack, Chase Gilmore, Bilal Khan e Minh Hong Tran. "Probability of the Loss of Offsite Power and Damage to Road Network due to a Rare Event". In 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icone20-power2012-54582.
Texto completo da fonteYue Qiu, Hong Zhou e Yueqin Wu. "An importance sampling method based on martingale with applications to rare event probability". In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4593574.
Texto completo da fonteM, Bittner, Broggi M e Beer M. "Rare Event Modelling for Stochastic Dynamic Systems approximated by the Probability Density Evolution Method". In Proceedings of the 29th European Safety and Reliability Conference (ESREL). Singapore: Research Publishing Services, 2019. http://dx.doi.org/10.3850/978-981-11-2724-3_0735-cd.
Texto completo da fonteXu, Yanwen, e Pingfeng Wang. "Sequential Sampling Based Reliability Analysis for High Dimensional Rare Events With Confidence Intervals". In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22146.
Texto completo da fonteBabanin, Alexander V. "Physics-Based Approach to Wave Statistics and Probability". In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10416.
Texto completo da fonteRelatórios de organizações sobre o assunto "Rare event probability"
Montalvo-Bartolomei, Axel, Bryant Robbins e Jamie López-Soto. Backward erosion progression rates from small-scale flume tests. Engineer Research and Development Center (U.S.), setembro de 2021. http://dx.doi.org/10.21079/11681/42135.
Texto completo da fonteBragdon, Sophia, Vuong Truong e Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), novembro de 2022. http://dx.doi.org/10.21079/11681/45902.
Texto completo da fonteRusso, David, Daniel M. Tartakovsky e Shlomo P. Neuman. Development of Predictive Tools for Contaminant Transport through Variably-Saturated Heterogeneous Composite Porous Formations. United States Department of Agriculture, dezembro de 2012. http://dx.doi.org/10.32747/2012.7592658.bard.
Texto completo da fonte