Academic literature on the topic 'Sobol sensitivity method'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Sobol sensitivity method.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Sobol sensitivity method"
1
Chen, Lixin, Zhongxiang Xu, Desuo Huang, and Zhining Chen. "An Improved Sobol Sensitivity Analysis Method." Journal of Physics: Conference Series 2747, no. 1 (2024): 012025. http://dx.doi.org/10.1088/1742-6596/2747/1/012025.
Full textAbstract:
Abstract The Sobol method is a variance-based global sensitivity analysis method that evaluates single-input and multiple-input interaction effects by calculating the contribution of a single input to the output variance and the contribution of multiple inputs to the output variance. The Sobol method requires each input to obey a uniform distribution of U [0,1], but it is difficult to meet the requirements in practice. Taking the sum function as an example, this paper analyzes the inapplicability of the existing Sobol method when the input does not obey the uniform distribution U [0,1]. To solve the inapplicability of the Sobol method and broaden the application scope, an improved Sobol sensitivity analysis method is proposed. First, the effect of the joint probability density function not 1 on sensitivity calculation is studied; second, the input parameters are changed to uniform distribution U [0,1] through variable substitution; finally, a complete algorithm model is presented and logical sensitivity analysis results are obtained. Application verification shows that the improved Sobol method is more scientific, applicable and practical.
2
Alves Borges, Luiz Felipe, Fabio Freitas Ferreira, Fábio Gonçalves, Antônio Espósito Junior, Aline Fernanda Da Silva Oliveira, and Wagner Rambaldi Telles. "IPSAL: Implementation of the module to generate the Sobol sequence and indices." VETOR - Revista de Ciências Exatas e Engenharias 33, no. 2 (2023): 60–69. http://dx.doi.org/10.14295/vetor.v33i2.16439.
Full textAbstract:
Sensitivity and uncertainty analysis hold significant importance across a range of applications, spanning from industrial problems to climate change, financial risk assessment, as well as mathematical and computational models. These analyses involve identifying influential input parameters in models to comprehend their impact on the output. Sensitivity analysis can be performed locally, examining parameter effects at a fixed value, or globally, evaluating the model across a range of parameter values. The Sobol method stands as a robust approach for global sensitivity analysis, employing a Sobol sequence to create samples more uniformly within the input parameter space, thus enabling efficient exploration of model inputs. This paper aims to introduce a computational implementation in Scilab to generate the Sobol sequence for utilization in sensitivity analysis through the Sobol method. A test case was applied to generate Sobol sequences and discuss the obtained results.
3
Herman, J. D., J. B. Kollat, P. M. Reed, and T. Wagener. "Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models." Hydrology and Earth System Sciences 17, no. 7 (2013): 2893–903. http://dx.doi.org/10.5194/hess-17-2893-2013.
Full textAbstract:
Abstract. The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over a six-month period in the Blue River watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly screen the most and least sensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. The method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.
4
Herman, J. D., J. B. Kollat, P. M. Reed, and T. Wagener. "Technical note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models." Hydrology and Earth System Sciences Discussions 10, no. 4 (2013): 4275–99. http://dx.doi.org/10.5194/hessd-10-4275-2013.
Full textAbstract:
Abstract. The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) model over a six-month period in the Blue River Watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly identify sensitive and insensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. Method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.
5
Pannier, Marie-Lise, Patrick Schalbart, and Bruno Peuportier. "Computationally efficient sensitivity analysis for building ecodesign with many-level categorical input factors." International Journal of Metrology and Quality Engineering 14 (2023): 15. http://dx.doi.org/10.1051/ijmqe/2023016.
Full textAbstract:
Dynamic building energy simulation and life cycle assessment (LCA) are useful ecodesign tools to mitigate the energy and environmental impacts of buildings. Various uncertain factors can affect the building energy and environmental modelling, including continuous and categorical factors (i.e., discrete factors without logical ranking). Sensitivity analysis (SA) is applied to identify the most influential factors on which additional research efforts are needed to increase the robustness of results. The Sobol method (Sobol) is the reference SA method, but it requires a significant amount of computation. Less time-consuming methods, such as an adaptation of the Morris screening (Morris), have shown a good ability to quantify the influence of factors, but their performance has not been investigated for categorical factors having many (more than two) levels. Two strategies (2LA-Morris and MA-Morris) based on the adaptation of Morris are proposed to handle many-level factors. Their performance is compared to that of Sobol based on four criteria: computation time, factor's relative influence, factor's ranking, and ability to detect interactions. For the LCA of a house including 24 uncertain factors, MA-Morris was able to quantify the influence of factors in the same way as Sobol, while reducing the computation time by a factor of 12.
6
Wang, Qi, Haiyang Li, Lin Lu, Luyi Yang, and Shenquan Wang. "Global Sensitivity Analysis of Earth-Moon Transfer Orbit Parameters Based on Sobol Method." International Journal of Aerospace Engineering 2022 (May 29, 2022): 1–22. http://dx.doi.org/10.1155/2022/6587890.
Full textAbstract:
In the process of Earth-Moon transfer orbit, many parameters are involved and the degree of influence varies among the parameters. How to accurately distinguish the influence relationship between different parameters is of great significance to engineering missions. Based on Sobol sequence sampling method and Sobol global sensitivity analysis method, a calculation process of global sensitivity analysis is proposed in this paper for high-fidelity Earth-Moon transfer orbit. A numerical simulation method is used to verify that the Sobol sequence sampling method has better convergence and higher precision than other sampling methods and has better adaptability in global sensitivity analysis. The effects of different state parameters and the combination of different parameters on the perilune parameters of Earth-Moon transfer orbit are given by simulation examples, which verifies the effectiveness and feasibility of the calculation process proposed in this paper. Simulation results show that the radial position and tangential velocity at the trans-lunar injection point are the main sensitivity parameters, and the other parameters have little effect on the results. The sensitivity of the orbital elements at the trans-lunar injection point to the perilune parameters is different and needs to be determined according to the specific parameters. The results of this study can provide important reference for future Earth-Moon transfer orbit design and related engineering missions.
7
Kalra, Tarandeep S., Alfredo Aretxabaleta, Pranay Seshadri, Neil K. Ganju, and Alexis Beudin. "Sensitivity analysis of a coupled hydrodynamic-vegetation model using the effectively subsampled quadratures method (ESQM v5.2)." Geoscientific Model Development 10, no. 12 (2017): 4511–23. http://dx.doi.org/10.5194/gmd-10-4511-2017.
Full textAbstract:
Abstract. Coastal hydrodynamics can be greatly affected by the presence of submerged aquatic vegetation. The effect of vegetation has been incorporated into the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. The vegetation implementation includes the plant-induced three-dimensional drag, in-canopy wave-induced streaming, and the production of turbulent kinetic energy by the presence of vegetation. In this study, we evaluate the sensitivity of the flow and wave dynamics to vegetation parameters using Sobol' indices and a least squares polynomial approach referred to as the Effective Quadratures method. This method reduces the number of simulations needed for evaluating Sobol' indices and provides a robust, practical, and efficient approach for the parameter sensitivity analysis. The evaluation of Sobol' indices shows that kinetic energy, turbulent kinetic energy, and water level changes are affected by plant stem density, height, and, to a lesser degree, diameter. Wave dissipation is mostly dependent on the variation in plant stem density. Performing sensitivity analyses for the vegetation module in COAWST provides guidance to optimize efforts and reduce exploration of parameter space for future observational and modeling work.
8
Derakhshanfard, Rasoul, Ali Nourali, Moein Taheri, and Hamed Safikhani. "Performance analysis in square cyclones using Sobol statistical sensitivity analysis method." Mechanic of Advanced and Smart Materials 2, no. 4 (2023): 401–12. http://dx.doi.org/10.52547/masm.2.4.401.
Full text
9
Guo, Jianbin, Shaohua Du, Yao Wang, and Shengkui Zeng. "Time-Dependent Global Sensitivity Analysis for Long-Term Degeneracy Model Using Polynomial Chaos." Advances in Mechanical Engineering 6 (January 1, 2014): 719825. http://dx.doi.org/10.1155/2014/719825.
Full textAbstract:
Global sensitivity is used to quantify the influence of uncertain model inputs on the output variability of static models in general. However, very few approaches can be applied for the sensitivity analysis of long-term degeneracy models, as far as time-dependent reliability is concerned. The reason is that the static sensitivity may not reflect the completed sensitivity during the entire life circle. This paper presents time-dependent global sensitivity analysis for long-term degeneracy models based on polynomial chaos expansion (PCE). Sobol’ indices are employed as the time-dependent global sensitivity since they provide accurate information on the selected uncertain inputs. In order to compute Sobol’ indices more efficiently, this paper proposes a moving least squares (MLS) method to obtain the time-dependent PCE coefficients with acceptable simulation effort. Then Sobol’ indices can be calculated analytically as a postprocessing of the time-dependent PCE coefficients with almost no additional cost. A test case is used to show how to conduct the proposed method, then this approach is applied to an engineering case, and the time-dependent global sensitivity is obtained for the long-term degeneracy mechanism model.
10
Sun, Xifu, Barry Croke, Stephen Roberts, and Anthony Jakeman. "Investigation of determinism-related issues in the Sobol′ low-discrepancy sequence for producing sound global sensitivity analysis indices." ANZIAM Journal 62 (December 7, 2021): C84—C97. http://dx.doi.org/10.21914/anziamj.v62.16094.
Full textAbstract:
A computationally efficient and robust sampling scheme can support a sensitivity analysis of models to discover their behaviour through Quasi Monte Carlo approximation. This is especially useful for complex models, as often occur in environmental domains when model runtime can be prohibitive. The Sobol' sequence is one of the most used quasi-random low-discrepancy sequences as it can explore the parameter space significantly more evenly than pseudo-random sequences. The built-in determinism of the Sobol' sequence assists in achieving this attractive property. However, the Sobol' sequence tends to deteriorate in the sense that the estimated errors are distributed inconsistently across model parameters as the dimensions of a model increase. By testing multiple Sobol' sequence implementations, it is clear that the deterministic nature of the Sobol' sequence occasionally introduces relatively large errors in sensitivity indices produced by well-known global sensitivity analysis methods, and that the errors do not diminish by averaging through multiple replications. Problematic sensitivity indices may mistakenly guide modellers to make type I and II errors in trying to identify sensitive parameters, and this will potentially impact model reduction attempts based on these sensitivity measurements. This work investigates the cause of the Sobol' sequence's determinism-related issues. References I. A. Antonov and V. M. Saleev. An economic method of computing LPτ-sequences. USSR Comput. Math. Math. Phys. 19.1 (1979), pp. 252–256. doi: 10.1016/0041-5553(79)90085-5 P. Bratley and B. L. Fox. Algorithm 659: Implementing Sobol’s quasirandom sequence generator. ACM Trans. Math. Soft. 14.1 (1988), pp. 88–100. doi: 10.1145/42288.214372 J. Feinberg and H. P. Langtangen. Chaospy: An open source tool for designing methods of uncertainty quantification. J. Comput. Sci. 11 (2015), pp. 46–57. doi: 10.1016/j.jocs.2015.08.008 on p. C90). S. Joe and F. Y. Kuo. Constructing Sobol sequences with better two-dimensional projections. SIAM J. Sci. Comput. 30.5 (2008), pp. 2635–2654. doi: 10.1137/070709359 S. Joe and F. Y. Kuo. Remark on algorithm 659: Implementing Sobol’s quasirandom sequence generator. ACM Trans. Math. Soft. 29.1 (2003), pp. 49–57. doi: 10.1145/641876.641879 W. J. Morokoff and R. E. Caflisch. Quasi-random sequences and their discrepancies. SIAM J. Sci. Comput. 15.6 (1994), pp. 1251–1279. doi: 10.1137/0915077 X. Sun, B. Croke, S. Roberts, and A. Jakeman. Comparing methods of randomizing Sobol’ sequences for improving uncertainty of metrics in variance-based global sensitivity estimation. Reliab. Eng. Sys. Safety 210 (2021), p. 107499. doi: 10.1016/j.ress.2021.107499 S. Tarantola, W. Becker, and D. Zeitz. A comparison of two sampling methods for global sensitivity analysis. Comput. Phys. Com. 183.5 (2012), pp. 1061–1072. doi: 10.1016/j.cpc.2011.12.015 S. Tezuka. Discrepancy between QMC and RQMC, II. Uniform Dist. Theory 6.1 (2011), pp. 57–64. url: https://pcwww.liv.ac.uk/~karpenk/JournalUDT/vol06/no1/5Tezuka11-1.pdf I. M. Sobol′. On the distribution of points in a cube and the approximate evaluation of integrals. USSR Comput. Math. Math. Phys. 7.4 (1967), pp. 86–112. doi: 10.1016/0041-5553(67)90144-9 I. M. Sobol′. Sensitivity estimates for nonlinear mathematical models. Math. Model. Comput. Exp 1.4 (1993), pp. 407–414.
Dissertations / Theses on the topic "Sobol sensitivity method"
1
Masinde, Brian. "Birds' Flight Range. : Sensitivity Analysis." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166248.
Full textAbstract:
’Flight’ is a program that uses flight mechanics to estimate the flight range of birds. This program, used by ornithologists, is only available for Windows OS. It requires manual imputation of body measurements and constants (one observation at a time) and this is time-consuming. Therefore, the first task is to implement the methods in R, a programming language that runs on various platforms. The resulting package named flying, has three advantages; first, it can estimate flight range of multiple bird observations, second, it makes it easier to experiment with different settings (e.g. constants) in comparison to Flight and third, it is open-source making contribution relatively easy. Uncertainty and global sen- sitivity analyses are carried out on body measurements separately and with various con- stants. In doing so, the most influential body variables and constants are discovered. This task would have been near impossible to undertake using ’Flight’. A comparison is made amongst the results from a crude partitioning method, generalized additive model, gradi- ent boosting machines and quasi-Monte Carlo method. All of these are based on Sobol’s method for variance decomposition. The results show that fat mass drives the simulations with other inputs playing a secondary role (for example mechanical conversion efficiency and body drag coefficient).
2
Alhossen, Iman. "Méthode d'analyse de sensibilité et propagation inverse d'incertitude appliquées sur les modèles mathématiques dans les applications d'ingénierie." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30314/document.
Full textAbstract:
Dans de nombreuses disciplines, les approches permettant d'étudier et de quantifier l'influence de données incertaines sont devenues une nécessité. Bien que la propagation directe d'incertitudes ait été largement étudiée, la propagation inverse d'incertitudes demeure un vaste sujet d'étude, sans méthode standardisée. Dans cette thèse, une nouvelle méthode de propagation inverse d'incertitude est présentée. Le but de cette méthode est de déterminer l'incertitude d'entrée à partir de données de sortie considérées comme incertaines. Parallèlement, les méthodes d'analyse de sensibilité sont également très utilisées pour déterminer l'influence des entrées sur la sortie lors d'un processus de modélisation. Ces approches permettent d'isoler les entrées les plus significatives, c'est à dire les plus influentes, qu'il est nécessaire de tester lors d'une analyse d'incertitudes. Dans ce travail, nous approfondirons tout d'abord la méthode d'analyse de sensibilité de Sobol, qui est l'une des méthodes d'analyse de sensibilité globale les plus efficaces. Cette méthode repose sur le calcul d'indices de sensibilité, appelés indices de Sobol, qui représentent l'effet des données d'entrées (vues comme des variables aléatoires continues) sur la sortie. Nous démontrerons ensuite que la méthode de Sobol donne des résultats fiables même lorsqu'elle est appliquée dans le cas discret. Puis, nous étendrons le cadre d'application de la méthode de Sobol afin de répondre à la problématique de propagation inverse d'incertitudes. Enfin, nous proposerons une nouvelle approche de la méthode de Sobol qui permet d'étudier la variation des indices de sensibilité par rapport à certains facteurs du modèle ou à certaines conditions expérimentales. Nous montrerons que les résultats obtenus lors de ces études permettent d'illustrer les différentes caractéristiques des données d'entrée. Pour conclure, nous exposerons comment ces résultats permettent d'indiquer les meilleures conditions expérimentales pour lesquelles l'estimation des paramètres peut être efficacement réalisée<br>Approaches for studying uncertainty are of great necessity in all disciplines. While the forward propagation of uncertainty has been investigated extensively, the backward propagation is still under studied. In this thesis, a new method for backward propagation of uncertainty is presented. The aim of this method is to determine the input uncertainty starting from the given data of the uncertain output. In parallel, sensitivity analysis methods are also of great necessity in revealing the influence of the inputs on the output in any modeling process. This helps in revealing the most significant inputs to be carried in an uncertainty study. In this work, the Sobol sensitivity analysis method, which is one of the most efficient global sensitivity analysis methods, is considered and its application framework is developed. This method relies on the computation of sensitivity indexes, called Sobol indexes. These indexes give the effect of the inputs on the output. Usually inputs in Sobol method are considered to vary as continuous random variables in order to compute the corresponding indexes. In this work, the Sobol method is demonstrated to give reliable results even when applied in the discrete case. In addition, another advancement for the application of the Sobol method is done by studying the variation of these indexes with respect to some factors of the model or some experimental conditions. The consequences and conclusions derived from the study of this variation help in determining different characteristics and information about the inputs. Moreover, these inferences allow the indication of the best experimental conditions at which estimation of the inputs can be done
3
Heredia, Guzman Maria Belen. "Contributions to the calibration and global sensitivity analysis of snow avalanche numerical models." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALU028.
Full textAbstract:
Une avalanche de neige est un danger naturel défini comme une masse de neige en mouvement rapide. Depuis les années 30, scientifiques conçoivent des modèles d'avalanche de neige pour décrire ce phénomène. Cependant, ces modèles dépendent de certains paramètres d'entrée mal connus qui ne peuvent pas être mesurés. Pour mieux comprendre les paramètres d'entrée du modèle et les sorties du modèle, les objectifs de cette thèse sont (i) de proposer un cadre pour calibrer les paramètres d'entrée et (ii) de développer des méthodes pour classer les paramètres d'entrée en fonction de leur importance dans le modèle en tenant compte la nature fonctionnelle des sorties. Dans ce cadre, nous développons des méthodes statistiques basées sur l'inférence bayésienne et les analyses de sensibilité globale. Nos développements sont illustrés sur des cas de test et des données réelles des avalanches de neige.D'abord, nous proposons une méthode d'inférence bayésienne pour récupérer la distribution des paramètres d'entrée à partir de séries chronologiques de vitesse d'avalanche ayant été collectées sur des sites de test expérimentaux. Nos résultats montrent qu'il est important d'inclure la structure d'erreur (dans notre cas l'autocorrélation) dans la modélisation statistique afin d'éviter les biais dans l'estimation des paramètres de frottement.Deuxièmement, pour identifier les paramètres d'entrée importants, nous développons deux méthodes basées sur des mesures de sensibilité basées sur la variance. Pour la première méthode, nous supposons que nous avons un échantillon de données et nous voulons estimer les mesures de sensibilité avec cet échantillon. Dans ce but, nous développons une procédure d'estimation non paramétrique basée sur l'estimateur de Nadaraya-Watson pour estimer les indices agrégés de Sobol. Pour la deuxième méthode, nous considérons le cadre où l'échantillon est obtenu à partir de règles d'acceptation/rejet correspondant à des contraintes physiques. L'ensemble des paramètres d'entrée devient dépendant du fait de l'échantillonnage d'acceptation-rejet, nous proposons donc d'estimer les effets de Shapley agrégés (extension des effets de Shapley à des sorties multivariées ou fonctionnelles). Nous proposons également un algorithme pour construire des intervalles de confiance bootstrap. Pour l'application du modèle d'avalanche de neige, nous considérons différents scénarios d'incertitude pour modéliser les paramètres d'entrée. Dans nos scénarios, la position et le volume de départ de l'avalanche sont les entrées les plus importantes.Nos contributions peuvent aider les spécialistes des avalanches à (i) prendre en compte la structure d'erreur dans la calibration du modèle et (ii) proposer un classementdes paramètres d'entrée en fonction de leur importance dans les modèles en utilisant des approches statistiques<br>Snow avalanche is a natural hazard defined as a snow mass in fast motion. Since the thirties, scientists have been designing snow avalanche models to describe snow avalanches. However, these models depend on some poorly known input parameters that cannot be measured. To understand better model input parameters and model outputs, the aims of this thesis are (i) to propose a framework to calibrate input parameters and (ii) to develop methods to rank input parameters according to their importance in the model taking into account the functional nature of outputs. Within these two purposes, we develop statistical methods based on Bayesian inference and global sensitivity analyses. All the developments are illustrated on test cases and real snow avalanche data.First, we propose a Bayesian inference method to retrieve input parameter distribution from avalanche velocity time series having been collected on experimental test sites. Our results show that it is important to include the error structure (in our case the autocorrelation) in the statistical modeling in order to avoid bias for the estimation of friction parameters.Second, to identify important input parameters, we develop two methods based on variance based measures. For the first method, we suppose that we have a given data sample and we want to estimate sensitivity measures with this sample. Within this purpose, we develop a nonparametric estimation procedure based on the Nadaraya-Watson kernel smoother to estimate aggregated Sobol' indices. For the second method, we consider the setting where the sample is obtained from acceptance/rejection rules corresponding to physical constraints. The set of input parameters become dependent due to the acceptance-rejection sampling, thus we propose to estimate aggregated Shapley effects (extension of Shapley effects to multivariate or functional outputs). We also propose an algorithm to construct bootstrap confidence intervals. For the snow avalanche model application, we consider different uncertainty scenarios to model the input parameters. Under our scenarios, the release avalanche position and volume are the most crucial inputs.Our contributions should help avalanche scientists to (i) account for the error structure in model calibration and (ii) rankinput parameters according to their importance in the models using statistical methods
4
Gilquin, Laurent. "Échantillonnages Monte Carlo et quasi-Monte Carlo pour l'estimation des indices de Sobol' : application à un modèle transport-urbanisme." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM042/document.
Full textAbstract:
Le développement et l'utilisation de modèles intégrés transport-urbanisme sont devenus une norme pour représenter les interactions entre l'usage des sols et le transport de biens et d'individus sur un territoire. Ces modèles sont souvent utilisés comme outils d'aide à la décision pour des politiques de planification urbaine.Les modèles transport-urbanisme, et plus généralement les modèles mathématiques, sont pour la majorité conçus à partir de codes numériques complexes. Ces codes impliquent très souvent des paramètres dont l'incertitude est peu connue et peut potentiellement avoir un impact important sur les variables de sortie du modèle.Les méthodes d'analyse de sensibilité globales sont des outils performants permettant d'étudier l'influence des paramètres d'un modèle sur ses sorties. En particulier, les méthodes basées sur le calcul des indices de sensibilité de Sobol' fournissent la possibilité de quantifier l'influence de chaque paramètre mais également d'identifier l'existence d'interactions entre ces paramètres.Dans cette thèse, nous privilégions la méthode dite à base de plans d'expériences répliqués encore appelée méthode répliquée. Cette méthode a l'avantage de ne requérir qu'un nombre relativement faible d'évaluations du modèle pour calculer les indices de Sobol' d'ordre un et deux.Cette thèse se focalise sur des extensions de la méthode répliquée pour faire face à des contraintes issues de notre application sur le modèle transport-urbanisme Tranus, comme la présence de corrélation entre paramètres et la prise en compte de sorties multivariées.Nos travaux proposent également une approche récursive pour l'estimation séquentielle des indices de Sobol'. L'approche récursive repose à la fois sur la construction itérative d'hypercubes latins et de tableaux orthogonaux stratifiés et sur la définition d'un nouveau critère d'arrêt. Cette approche offre une meilleure précision sur l'estimation des indices tout en permettant de recycler des premiers jeux d'évaluations du modèle. Nous proposons aussi de combiner une telle approche avec un échantillonnage quasi-Monte Carlo.Nous présentons également une application de nos contributions pour le calage du modèle de transport-urbanisme Tranus<br>Land Use and Transportation Integrated (LUTI) models have become a norm for representing the interactions between land use and the transportation of goods and people in a territory. These models are mainly used to evaluate alternative planning scenarios, simulating their impact on land cover and travel demand.LUTI models and other mathematical models used in various fields are most of the time based on complex computer codes. These codes often involve poorly-known inputs whose uncertainty can have significant effects on the model outputs.Global sensitivity analysis methods are useful tools to study the influence of the model inputs on its outputs. Among the large number of available approaches, the variance based method introduced by Sobol' allows to calculate sensitivity indices called Sobol' indices. These indices quantify the influence of each model input on the outputs and can detect existing interactions between inputs.In this framework, we favor a particular method based on replicated designs of experiments called replication method. This method appears to be the most suitable for our application and is advantageous as it requires a relatively small number of model evaluations to estimate first-order or second-order Sobol' indices.This thesis focuses on extensions of the replication method to face constraints arising in our application on the LUTI model Tranus, such as the presence of dependency among the model inputs, as far as multivariate outputs.Aside from that, we propose a recursive approach to sequentially estimate Sobol' indices. The recursive approach is based on the iterative construction of stratified designs, latin hypercubes and orthogonal arrays, and on the definition of a new stopping criterion. With this approach, more accurate Sobol' estimates are obtained while recycling previous sets of model evaluations. We also propose to combine such an approach with quasi-Monte Carlo sampling.An application of our contributions on the LUTI model Tranus is presented
5
Sohier, Henri. "Modélisation, analyse et optimisation d’un largage de fusée spatiale depuis un porteur de type avion." Thesis, Toulouse, ISAE, 2014. http://www.theses.fr/2014ESAE0044/document.
Full textAbstract:
Un système de lancement aéroporté est constitué d'un porteur de type avion larguant un lanceur à une certaine altitude. De tels systèmes sont l'objet d'un intérêt croissant, notamment pour la mise à poste de petits satellites. Les travaux présentés dans cette thèse s'intègrent dans le programme Perseus du CNES qui a déjà donné lieu à la construction d'un modèle réduit appelé EOLE. Il s'agit d'étudier la phase de largage, particulièrement sensible.Les contraintes de similitude pouvant permettre l'étude du largage taille réelle avec EOLE sont d'abord identifiées. Les possibilités d'extrapolation directe et déterministe des mesures réalisées avec EOLE étant limitées par le non respect d'une contrainte de masse, il est choisi d'étudier le largage avec une approche probabiliste en développant un nouveau modèle multi-corps. Une grande variété d'incertitudes est prise en compte, concernant par exemple aussi bien les interactions aérodynamiques que le mécanisme de séparation. Un nouveau critère de performance générique,basé sur des géométries élémentaires, est développé pour évaluer la fiabilité du largage.L'analyse de sensibilité du largage aux facteurs d'incertitude est ensuite réalisée. Compte tenu du nombre élevé de paramètres en jeu et du temps de simulation, il est d'abord recherché une simplification du modèle. La méthode de Morris est utilisée pour identifier des facteurs d'incertitude peu influents pouvant être fixés à une certaine valeur. Cette étape est fréquente, mais il est montré qu'il existe un risque important de fixer des facteurs dont l'influence a en fait été sous-estimée. Une adaptation de la méthode de Morris améliorant l'échantillonnage des facteurs, le calcul de leurs influences et le traitement statistique des résultats permet de réduire considérablement ce risque.Une fois l'impact des différentes incertitudes estimé, il a été possible d'optimiser les conditions de largage afin de réduire la probabilité qu'un problème intervienne<br>In an air launch to orbit, a space rocket is launched from a carrier aircraft. Air launchto orbit appears as particularly interesting for small satellites. This Ph.D. thesis is part of the program Pegasus of the French space agency CNES and it follows the development of a small scale demonstrator called EOLE. It focuses on the very sensitive separation phase.The similitude constraints which have to be respected to study the large scale system with EOLEare first identified. A problem of mass limits the possibilities to directly extrapolate at a larger scale, in a deterministic approach, data obtained with EOLE. It is decided to study the separation in a probabilistic approach by developing a new multi-body model. A great variety of uncertainties are taken into account, from the aerodynamic interactions to the atmospheric turbulences, the separation mechanism, and the launch trajectories. A new performance criterion is developed to quantify the safety of the separation phase. It is based on elementary geometries and it could beused in other contexts.A sensitivity analysis is applied to estimate the influence of the uncertainties on the performance criterion. Given the large number of factors of uncertainty and the non-negligible simulation time,the model is first simplified. The Morris method is applied to identify the factors with a low influence which can be fixed to a given value. It is a frequent step, but it is shown that there isa high risk to fix the wrong factors. Any further study would then be altered. The risk to fix the wrong factors is significantly reduced by improving the factors sampling, the calculation of their influence, and the statistical treatment of the results. This new method is used to estimate the influence of the uncertainties at the separation and the safety is improved by optimizing launch trajectories
6
Garcia, Hernandez Elizabeth Antonia. "Analyse de sensibilité globale appliquée à l'évaluation des risques thermiques Kinetic modeling using temperature as an on-line measurement: application to the hydrolysis of acetic anhydride, a revisited kinetic model." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR10.
Full textAbstract:
L'emballement thermique est l'un des principaux évènements critiques des accidents de l'industrie chimique. Pour évaluer le risque de tels évènements, une évaluation du risque thermique doit être effectuée. Néanmoins, sur la base de l'évaluation du risque thermique, il n'est pas possible de connaitre les entrées du modèle les plus influentes sur le risque thermique. L'analyse de sensibilité globale a été proposée comme une nouvelle perspective pour évaluer l'influence et l'interaction des entrées du modèle sur les paramètres de risque thermique. Les paramètres suivants ont été étudiés : température maximale de réaction, élévation de température et temps pour atteindre la température maximale de réaction. La méthode a été appliquée a deux systèmes de réaction : un système en phase homogène avec une seule réaction, l’hydrolyse de l'anhydride acétique et un système en milieu diphasique avec plusieurs réactions, l’époxidation de l'huile de coton<br>Thermal runaway is one of the main critical events in chemical industry accidents. To evaluate the risk of such events, thermal risk assessment, must be done. Nevertheless, based on thermal risk assessment, it is not possible to know the most influence model inputs on the thermal risk. Global sensitivity analysis was proposed as a new perspective to evaluate the influence and the interaction of the model inputs on thermal risk parameters. The following parameters were studied: maximum reaction temperature, temperature rise and time to reach the maximum reaction temperature. The method was applied to two reaction systems: a homogeneous phase system with a single reaction, hydrolysis of acetic anhydride, and a two-phase system with several reactions, epoxidation of cottonseed oil
7
Novák, Lukáš. "Pravděpodobnostní modelování smykové únosnosti předpjatých betonových nosníků: Citlivostní analýza a semi-pravděpodobnostní metody návrhu." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2018. http://www.nusl.cz/ntk/nusl-372051.
Full textAbstract:
Diploma thesis is focused on advanced reliability analysis of structures solved by non--linear finite element analysis. Specifically, semi--probabilistic methods for determination of design value of resistance, sensitivity analysis and surrogate model created by polynomial chaos expansion are described in the diploma thesis. Described methods are applied on prestressed reinforced concrete roof girder.
8
Lu, Rong. "Statistical Methods for Functional Genomics Studies Using Observational Data." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1467830759.
Full text
9
Paleari, L. "IN SILICO IDEOTYPING: DEFINITION AND EVALUATION OF RICE IDEOTYPES IMPROVED FOR RESISTANCE/TOLERANCE TRAITS TO BIOTIC AND ABIOTIC STRESSORS UNDER CLIMATE CHANGE SCENARIOS." Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/483333.
Full textAbstract:
The development of new cultivars better adapted to specific growing conditions is a key strategy to meet an ever-increasing growing global food demand and search for more sustainable cropping systems. This is even more crucial in the context of a changing climate.
Ecophysiological models and advanced computational techniques (e.g., sensitivity analysis, SA) represent powerful tools to analyze genotype (G) by environment (E) interactions, thus supporting breeders in identifying key traits for specific agro-environmental contexts. However, limits for the effective use of mathematical models within breeding programs are represented by the uncertainty in the distribution of plant trait values, the lack of processes dealing with resistance/tolerance traits in most ideotyping studies, the partial suitability of current crop models for ideotyping purposes, and the absence of modelling tools directly usable by breeders.
The aim of this research was to address these issues improving methodologies already in use, proposing new paradigms for the development of crop models explicitly targeting ideotyping applications and developing tools that would encourage a deep interaction of the modelling and breeding communities. The focus was on rice, for its role as staple food for more than a half of world’s population, and on resistance/tolerance traits to biotic/abiotic stressors, for their central role in increasing crop adaptation. Moreover, current conditions and climate change projections were considered, to support the definition of strategies for breeding in the medium-long term.
A standard procedure to quantify − and manage − the impact of the uncertainty in the distribution of plant trait values was developed, using the WARM rice model and the Sobol’ method as case study. The approach is based on a SA (generating sample of parameter distributions) of a SA (generating samples of parameters for each generated distribution) using distributions of jackknife statistics calculated on literature values to reproduce the uncertainty in defining parameters distributions. As a practical implication, the procedure developed allows identifying plant traits whose uncertainty in distribution can alter ideotyping results, i.e., traits whose distributions could need to be refined.
Global SA was then used to identify rice traits putatively producing the largest yield benefits in five contrasting districts in the Philippines, India, China, Japan and Italy. The analysis involved phenotypic traits dealing with light interception, photosynthetic efficiency, tolerance to abiotic stressors, resistance to fungal pathogens and grain quality. Results suggested that breeding for traits involved with disease resistance and tolerance to cold- and heat-induced spikelet sterility could provide benefits similar to those obtained from improving traits affecting potential yield. Instead, advantages resulting from varying traits involved with grain quality were markedly frustrated by inter-annual weather variability. Since results highlighted strong G×E interactions, a new index to derive district-specific ideotypes was developed.
Given the key role of biotic/abiotic stressors in determining actual yield and the deep impact of related G×E interactions, a study was carried out by explicitly focusing on the definition of rice ideotypes improved for their resistance to fungal pathogens and tolerance to abiotic constraints (temperature shocks inducing sterility). The analysis was carried out at district level with a high spatial resolution (5 km × 5 km elementary simulation unit), targeting the improvement of the most representative 34 varieties in six Italian rice districts. Genetic improvement was simulated via the introgression of traits from donor varieties. Results clearly showed that breeders should focus on increasing resistance to blast disease, as this appears as a factor markedly limiting rice yields in Italy, regardless of the districts and climate scenarios, whereas benefits deriving from improving tolerance to cold-induced sterility could be markedly affected by G×E interactions.
To reduce the risk of discrepancies between in silico ideotypes and their in vivo realizations, both studies involved only model parameters with a close relationship with phenotypic traits breeders are working on. However, a long-term strategy to overcome limitations related with the partial suitability of available models would be building new ideotyping-specific models explicitly around traits involved in breeding programs. This proposal for a paradigm shift in model development was illustrated taking salt stress tolerance and rice as a case study. Dedicated growth chamber experiments were conducted to develop a new model explicitly accounting for tolerance traits modulating Na+ uptake and distribution in plant tissues, as well as the impact of the accumulated Na+ on photosynthesis, senescence and spikelet sterility. An ideotyping study was conducted at two sites (in Greece and California) characterized by different seasonal dynamics of salinity in field water. Results showed how, under different scenarios, traits assuring the largest contribution to the overall tolerance could refer to completely different physiological mechanisms: tissue tolerance in one case, sodium exclusion in the other. This encourages the development of explicit trait-based approaches to increase the integration of crop models within breeding programs.
A parallel path to achieve this goal is the development of modelling platforms targeting breeders as final users, who does not have necessarily in-depth skills in crop modelling and IT. The platform ISIde, derived from a close collaboration between target users, biophysical modelers and IT specialists, represents the first prototype of a platform specifically developed for being used directly by breeders to evaluate in silico improved varieties at district level.
This thesis demonstrated the usefulness of simulation models for the definition of ideotypes for specific agro-environmental conditions. Targeting ideotyping applications, new methodologies, paradigms for model development and modelling tools were developed, thus contributing to improve the potential of crop modelling to support breeding programs. Future developments will target researches aimed at overcoming the limits behind this study, i.e., (i) absence of explicit interactions between traits, (ii) no adaptation strategies considered, and (iii) lack of approaches for the simulation of the evolutionary potential of pathogens in response to long-term climate variations and increased host resistance.
10
Riahi, Hassen. "Analyse de structures à dimension stochastique élevée : application aux toitures bois sous sollicitation sismique." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2013. http://tel.archives-ouvertes.fr/tel-00881187.
Full textAbstract:
Le problème de la dimension stochastique élevée est récurrent dans les analyses probabilistes des structures. Il correspond à l'augmentation exponentielle du nombre d'évaluations du modèle mécanique lorsque le nombre de paramètres incertains est élevé. Afin de pallier cette difficulté, nous avons proposé dans cette thèse, une approche à deux étapes. La première consiste à déterminer la dimension stochastique efficace, en se basant sur une hiérarchisation des paramètres incertains en utilisant les méthodes de criblage. Une fois les paramètres prépondérants sur la variabilité de la réponse du modèle identifiés, ils sont modélisés par des variables aléatoires et le reste des paramètres est fixé à leurs valeurs moyennes respectives, dans le calcul stochastique proprement dit. Cette tâche fut la deuxième étape de l'approche proposée, dans laquelle la méthode de décomposition de la dimension est utilisée pour caractériser l'aléa de la réponse du modèle, par l'estimation des moments statistiques et la construction de la densité de probabilité. Cette approche permet d'économiser jusqu'à 90% du temps de calcul demandé par les méthodes de calcul stochastique classiques. Elle est ensuite utilisée dans l'évaluation de l'intégrité d'une toiture à ossature bois d'une habitation individuelle installée sur un site d'aléa sismique fort. Dans ce contexte, l'analyse du comportement de la structure est basée sur un modèle éléments finis, dans lequel les assemblages en bois sont modélisés par une loi anisotrope avec hystérésis et l'action sismique est représentée par huit accélérogrammes naturels fournis par le BRGM. Ces accélérogrammes permettent de représenter différents types de sols selon en se référant à la classification de l'Eurocode 8. La défaillance de la toiture est définie par l'atteinte de l'endommagement, enregistré dans les assemblages situés sur les éléments de contreventement et les éléments d'anti-flambement, d'un niveau critique fixé à l'aide des résultats des essais. Des analyses déterministes du modèle éléments finis ont montré que la toiture résiste à l'aléa sismique de la ville du Moule en Guadeloupe. Les analyses probabilistes ont montré que parmi les 134 variables aléatoires représentant l'aléa dans le comportement non linéaire des assemblages, 15 seulement contribuent effectivement à la variabilité de la réponse mécanique ce qui a permis de réduire la dimension stochastique dans le calcul des moments statistiques. En s'appuyant sur les estimations de la moyenne et de l'écart-type on a montré que la variabilité de l'endommagement dans les assemblages situés dans les éléments de contreventement est plus importante que celle de l'endommagement sur les assemblages situés sur les éléments d'anti-flambement. De plus, elle est plus significative pour les signaux les plus nocifs sur la structure.
Book chapters on the topic "Sobol sensitivity method"
1
Jiang, Yuze, Wenyu Yang, Liang Qin, and Tong Ding. "Geometric Error Modeling and Sensitivity Analysis of a Laser Pipe-Cutting System Based on Lie Group and Sobol Method." In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85910-7_49.
Full text
2
Bingler Arnold and Bilicz Sándor. "Sensitivity Analysis Using a Sparse Grid Surrogate Model in Electromagnetic NDE." In Studies in Applied Electromagnetics and Mechanics. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-836-5-152.
Full textAbstract:
The global sensitivity analysis of electromagnetic nondestructive evaluation (NDE) by means of Sobol' indices are considered in this work. To reduce the computational burden, a sparse grid surrogate model is used. The latter can simply replace the true simulator to some extent, but it can also be used to numerically evaluate the integrals defining the Sobol' indices. In most of the NDE setups, the output is not a scalar quantity but functional data (e.g., a surface scan); a method is presented to take this into account. The sparse grid based sensitivity analysis is compared to classical techniques via examples drawn from electromagnetic NDE.
3
Bilicz, Sándor. "Sensitivity Analysis for the Inverse Problems of Electromagnetic Nondestructive Evaluation." In Studies in Applied Electromagnetics and Mechanics. IOS Press, 2020. http://dx.doi.org/10.3233/saem200032.
Full textAbstract:
Sensitivity analysis of the model-based inverse problem associated to electromagnetic nondestructive evaluation is dealt with. Some uncertainty of the arrangement is inevitable present (imprecise host material parameters, sensor mispositioning, etc.), and this induces uncertainty on the reconstructed defect parameters. The aim of this work is to present a methodology for the ranking of the different sources of random error according to their contribution to the reconstruction uncertainty. To this end, state-of-art mathematical tools of sensitivity analysis are applied, including Sobol’ indices, and a polynomial chaos expansion surrogate model to reduce the computational burden of the method. A numerical example drawn from magnetic flux leakage nondestructive evaluation is presented to illustrate the proposed methodology.
4
"Pacific Salmon Environmental and Life History Models: Advancing Science for Sustainable Salmon in the Future." In Pacific Salmon Environmental and Life History Models: Advancing Science for Sustainable Salmon in the Future, edited by Paul McElhany, E. Ashley Steel, David Jensen, and Karen A. Avery. American Fisheries Society, 2009. http://dx.doi.org/10.47886/9781934874097.ch17.
Full textAbstract:
<em>Abstract.</em>—The Ecosystem Diagnosis and Treatment (EDT) model has been widely applied in developing recovery goals and in identifying recovery actions for Pacific salmonids. We conducted a sensitivity analysis of this complex model to estimate prediction intervals and to determine which parameter uncertainties have the greatest influence on model output precision. We used the global sensitivity method of Sobol, but applied a hierarchical structuring of model parameters because the model incorporates a very large number of parameters. Initial results suggest that the model prediction intervals are large enough to affect management decisions and that the model may be most sensitive to parameters that cannot be controlled by user input. Sensitivity analysis of large, complex models like EDT is both possible and necessary for making robust natural resource management decisions.
5
Coveney, Peter V., and Shunzhou Wan. "Verification, Validation and Uncertainty Quantification." In Molecular Dynamics: Probability and Uncertainty. Oxford University PressOxford, 2025. https://doi.org/10.1093/9780191997099.003.0005.
Full textAbstract:
Abstract This chapter presents a comprehensive overview of Verification, Validation and Uncertainty Quantification (VVUQ) in the context of molecular dynamics. It begins with an introduction to the fundamental concepts of VVUQ, followed by detailed explanations of various theoretical methods including stochastic collocation, polynomial chaos, Sobol indices and Gaussian processes. The chapter outlines standard procedures for verification and validation, emphasizing their critical role in ensuring the accuracy and reliability of simulations. It delves into techniques for uncertainty quantification specific to molecular dynamics, discussing software tools and services that facilitate these processes. Furthermore, the chapter explores the impact of interaction potential parameters on simulation outcomes, addressing both epistemic and aleatoric uncertainties. Advanced methods such as derivative-free deep active subspace, kernel-based approaches and global sensitivity analysis are examined for their effectiveness in high-dimensional parameter spaces. The chapter concludes with a summary of key insights and future directions for VVUQ in molecular dynamics.
6
"Odour quality depends not only on the sensitivity of the human nose but also on the subjectivity of the human language to be able to describe the odour (17). Some chemical characteristics of slurry have been compared to the slurry odour. A relationship between volatile fatty acids (VFA) and odour offensiveness of poultry manure was described by Bell (18). Sobel (19) used a panel of people to assess the odour offensiveness of poultry manure and found a correlation between total solids and offensiveness. Williams (5) found that odour intensity measurements by liquid dilution of piggery slurry were not successful, and concluded that the offensiveness of odours rather than their Intensity was the best indicator of odour nuisance.Odour offensiveness is a supra-threshold assessment and depends on the human sense of smell. Since complaints result from the detection of offensive compounds by the human nose and because of the problems of quantifying all the odour compounds in slurry by analytical methods, it is desirable to use the human nose for odour offensiveness measurements. This presents problems, since there is a large variation of sensitivity to odorants and some people are anosmic & 20). The well being of a person also affects their response to odours (2). Odour offensiveness is a subjective determination and requires as many people as possible to assess an odour and minimise variations in sensitivity. The problems of routine olfactory odour assessments are that they are not only very time consuming, but also very subjective. Spoelstra (21) listed criteria for potentially suitable odour indicators and concluded that VFA and p-cresol were the best odour indicators of piggery slurry. Williams (5) examined the supernatant 5 day biochemical oxygen demand (BOD ), total organic acids (TOA), VFA and." In Odour Prevention and Control of Organic Sludge and Livestock Farming. CRC Press, 1986. http://dx.doi.org/10.1201/9781482286311-119.
Full textConference papers on the topic "Sobol sensitivity method"
1
Gao, Tian, Youkang Zhang, Linlin Dong, et al. "Sensitivity Analysis on Efficiency Influence Factors of Transmission Chain Slope Gravity Energy Storage System Based on Sobol Method." In 2024 IEEE International Conference on Electrical Energy Conversion Systems and Control（IEECSC）. IEEE, 2024. https://doi.org/10.1109/ieecsc62814.2024.10913819.
Full text
2
Liu, Qiang, Baiwei Feng, Zuyuan Liu, Zhailiu Hao, and Heng Zhang. "The improvement of sobol' sensitivity analysis method." In 2015 First International Conference on Reliability Systems Engineering (ICRSE). IEEE, 2015. http://dx.doi.org/10.1109/icrse.2015.7366460.
Full text
3
Li, Chenzhao, and Sankaran Mahadevan. "Global Sensitivity Analysis for a Bayesian Network." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59671.
Full textAbstract:
In a Bayesian network, how a node of interest is affected by the observation of another node is of interest in both forward propagation and backward inference. The proposed global sensitivity analysis (GSA) for Bayesian network aims to calculate the Sobol’ sensitivity index of a node with respect to the node of interest. The desired GSA for Bayesian network confronts two challenges. First, the computation of the Sobol’ index requires a deterministic function while the Bayesian network is a stochastic model. Second, the computation of the Sobol’ index can be expensive, especially if the model inputs are correlated, which is common in a Bayesian network. To solve the first challenge, this paper uses the auxiliary variable method to convert the path between two nodes in the Bayesian network to a deterministic function, thus making the Sobol’ index computation feasible in a Bayesian network. To solve the second challenge, this paper proposes an efficient algorithm to directly estimate the first-order Sobol’ index from Monte Carlo samples of the prior distribution of the Bayesian network, so that the proposed GSA for Bayesian network is computationally affordable. Before the updating, the proposed algorithm can predict the uncertainty reduction of the node of interest purely using the prior distribution samples, thus providing quantitative guidance for effective observation and updating.
4
Zhao, Zhiqiang, Zhigang Wang, and Liu Yi. "Study on methods based on Sobol indices method for analyzing sensitivity of army equipment effectiveness." In International Conference on Simulation and Modeling Methodologies, Technologies and Applications. WIT Press, 2014. http://dx.doi.org/10.2495/smta140851.
Full text
5
Deng, Liting, Liping Yan, Zihan Sun, Xiang Zhao, and Richard Xian-Ke Gao. "Global Sensitivity Analysis for Crosstalk of Three-Conductor Transmission Line using Sobol ′ Method." In 2022 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC). IEEE, 2022. http://dx.doi.org/10.1109/apemc53576.2022.9888666.
Full text
6
Waqas, Abi, Daniele Melati, and Andrea Melloni. "Stochastic simulation and sensitivity analysis of photonic circuit through Morris and Sobol method." In Optical Fiber Communication Conference. OSA, 2017. http://dx.doi.org/10.1364/ofc.2017.th2a.3.
Full text
7
Qiao, Zhongfei, Rongfeng Yang, Weiqiang Liao, Yibin Fang, Yingjun Gui, and Xin Dong. "Sensitivity analysis of the impedance parameters of converters based on the Sobol method." In Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), edited by Mohd Shakir Md Saat and Mamun Bin Ibne Reaz. SPIE, 2023. http://dx.doi.org/10.1117/12.3004249.
Full text
8
Song, Wenqiang, Fei Wang, Jiandong Duan, and Shuai Fang. "Loss Sensitivity Analysis in Distribution Networks Considering the Influence of Power Quality Using Sobol Sensitivity Analysis Method." In 2022 4th International Conference on Power and Energy Technology (ICPET). IEEE, 2022. http://dx.doi.org/10.1109/icpet55165.2022.9918323.
Full text
9
Chen, Chen, Su-Dan Huang, Guang-Zhong Cao, Gang Jing, and Liu Yan. "Thrust Force Ripple Analysis of Linear Switched Reluctance Motors Using Sobol Sensitivity Analysis Method." In 2021 13th International Symposium on Linear Drives for Industry Applications (LDIA). IEEE, 2021. http://dx.doi.org/10.1109/ldia49489.2021.9505782.
Full text
10
Bakhtiari-Nejad, Firooz, Arastou Azimi, and Robert G. Parker. "Sensitivity Analysis of a Flexible Marine Riser Using Sobol’s Method." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-72502.
Full textAbstract:
In this research, sensitivity analysis of upper and lower end angles in addition to the midpoint displacement of the flexible marine riser with respect to structural parameters such as: uniform mass per unit length, external diameter and the tension of the riser are conducted. Harsh environmental circumstances of ocean flow in addition to exerted tension on top of risers may lead to irreparable damages, so it is important to have a parametric study of dynamic response before it is controlled. The “Sobol” method is applied here as a reliable statistical method to sensitivity analysis of a flexible system. Motion equation of the system is developed based on Hamilton’s principle. The riser is modeled as a distributed parameter system. Moreover, simulations are carried out based on Assumed Mode Method (AMM) to solve PDE of the riser through mode shapes and generalized coordinates. Finally, the results of sensitivity analysis are presented.