The formalism of rare event probability estimation in complex systems

Mitard, J.; Balesdent, Mathieu

doi:10.1016/b978-0-08-100091-5.00003-4

Cited by 6 publications

(10 citation statements)

References 7 publications

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“…A major study by J. Morio, described in the book [28], included a section devoted to the problem of determining LVs' SP drop areas using various calculation methods. The problem included the determination of the initial parameters, such as the separation altitude, velocity fluctuations, trajectory, separation angle azimuth, LV mass, and weather fluctuations.…”

Section: Monte Carlo Methods and Alternative Optionsmentioning

confidence: 99%

Methods for the Calculation and Control of Launch Vehicle Drop Regions

et al. 2023

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The article aims at reviewing the drop regions (DR) of the launch vehicles (LV) separating parts (SP) and methods of their determination. The DRs include sea and land areas; going beyond them is associated with a number of environmental, economic, and political factors. Their combination dictates the need to ensure the safety of the people, transport, infrastructure, and environment from the negative impact of LV SPs and fuel residues. The Monte Carlo method is mostly used to determine the impact areas. It enables an estimation of the probability of the SPs of LVs falling in certain areas, constituting the DRs. These points are varied according to a set of different initial parameters. The methods of controlling the impact areas are contingently divided into engineering (based on a change in the design appearance of the LV), mathematical (which includes the changes in or optimization of the LV’s trajectory or its SP), and “soft landing” (implying the return of the LV’s to the spaceport or to a certain prepared area). The present analysis can be used as a starting point when choosing a method for determining and controlling the projected LVs and the SPs’ area of impact.

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Section: Monte Carlo Methods and Alternative Optionsmentioning

confidence: 99%

Methods for the Calculation and Control of Launch Vehicle Drop Regions

et al. 2023

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“…On the other hand, some works (Bertrand et al, 2017) provide probabilistic estimation methods of an on-ground collision knowing that the drone is unable to ensure flight continuation. The assessment provided by these methods is performed thanks to Monte Carlo (Morio and Balesdent, 2015) simulation. However the computational effort to estimate the probability of rare events with a high confidence using standard Monte Carlo (MC) method becomes intractable.…”

Section: Problem Statementmentioning

confidence: 99%

“…The main contribution of this paper is a comprehensive tooled method to estimate the onground collision probability by considering the contribution of on-board failures, reconfiguration mechanisms and operational specificity. To tackle Monte Carlo limitations, variance reduction methods (Morio and Balesdent, 2015) and more specifically Importance Sampling (IS) is used to obtain quicker and tighter estimation of the probability than standard Monte Carlo method. The paper provides a detailed presentation of the method and a demonstration of Importance Sampling benefits over Monte Carlo through a comparative study on a UAS case study.…”

Section: Contributionsmentioning

confidence: 99%

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A Comprehensive Probabilistic Assessment Method of UAS Ground Collision Risk

Serru¹,

Delmas²

2021

Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

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Unmanned Aircraft Systems are widely experienced but using these systems for missions near to populated areas raises new safety challenges. To address these challenges, the European Aviation Safety Agency requires assessing, for a given operational profile, the likelihood of on-ground collision with critical infrastructure or people. Various works use Model Based Safety Assessment to identify the failure contributing to the crash, while some works provide probabilistic estimation methods of an on-ground collision. In these methods the assessment is performed thanks to Monte Carlo simulation known to be time-consuming to estimate the probability of rare events. This paper will thus provide a comprehensive tooled method to estimate the on-ground collision probability and uses Importance Sampling method to tackle Monte Carlo limitations. Through a comparative study based on a drone usecase, this paper provides a demonstration of the benefits of Importance Sampling over Monte Carlo method. Indeed, this method allows a reduction in the number of simulations and thus the time needed to compute probabilities, with a high confidence in the results. The experiments are based on a safety model formalized with the Open AltaRica platform on an ad-hoc simulator to perform both Monte Carlo and important sampling simulations.

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“…This larger set of samples in turn allows for more accurate estimates of the probability of occurrence of the event, or to obtain the same accuracy with less numerical effort. Originally developed in statistical physics (Kahn & Harris, 1951), RES algorithms have found applications in, for example, queuing networks (Heidelberger, 1995), biochemistry (Kuwahara & Mura, 2008), and aircraft design (Morio & Balesdent, 2015). In Earth system modeling, rare event algorithms have previously been successfully applied to atmospheric models, to over‐sample heat waves (Ragone & Bouchet, 2021; Ragone et al., 2017) and extremely intense tropical cyclones (Webber et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Rare Event Simulation of Extreme European Winter Rainfall in an Intermediate Complexity Climate Model

Wouters

Schiemann

Shaffrey

2023

J Adv Model Earth Syst

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The impact of anthropogenic climate change is felt most acutely during extreme weather and climate events, such as windstorms, heavy precipitation, flooding, heatwaves, and drought. Applied research in extremes addresses the questions of (a) quantifying the probability of extreme event occurrence and detecting changes over time, (b) attributing such changes to natural variability as well as natural and anthropogenic forcing, including event attribution for individual observed extremes, (c) predicting occurrence probabilities at lead times of days to decades, and (d) deriving centennial and longer climate-change projections (Seneviratne et al., 2021). Designing and interpreting these kinds of studies is underpinned by, and advances, understanding of the physical processes involved.All of these applications are also heavily reliant on computer simulation. For example, large ensembles of global climate model simulations are driven with transient historical and scenario forcings to detect forced changes in the historical period and in projected future climates (Deser et al., 2020), and systematic variation of the natural and anthropogenic forcings used in model experiments allows for the attribution of trends in extremes to these forcings (Wan et al., 2019). Similarly, event attribution contrasts the occurrence of specific historically observed extremes in ensembles of all-forcings simulations and counterfactual simulations in which changing anthropogenic forcings are disregarded (Otto et al., 2018). In forecast applications, ensembles of simulations are initialized with observation-based estimates of the state of the atmosphere, ocean, cryosphere, and land surface with one aim to quantify changes of extreme event occurrence probability in the forecast period compared to a climatological baseline (Davini et al., 2021).Conducting these model experiments is costly in terms of human and computer time, data analysis and storage, and energy consumption. The nature and rareness of the investigated events imposes competing demands on a modeling setup that is fit-for-purpose in terms of model complexity, the length and number of simulations (ensembles members), and resolution. A case in point are seasonal precipitation extremes in the midlatitudes (M. D. K.

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The formalism of rare event probability estimation in complex systems

Cited by 6 publications

References 7 publications

Methods for the Calculation and Control of Launch Vehicle Drop Regions

Methods for the Calculation and Control of Launch Vehicle Drop Regions

A Comprehensive Probabilistic Assessment Method of UAS Ground Collision Risk

Rare Event Simulation of Extreme European Winter Rainfall in an Intermediate Complexity Climate Model

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