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Relevant bibliographies by topics / Propagation of uncertainty

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Academic literature on the topic 'Propagation of uncertainty'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Propagation of uncertainty"

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Barrio, R., M. Rodríguez, A. Abad, and S. Serrano. "Uncertainty propagation or box propagation." Mathematical and Computer Modelling 54, no. 11-12 (December 2011): 2602–15. http://dx.doi.org/10.1016/j.mcm.2011.06.036.

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Sciacchitano, Andrea, and Bernhard Wieneke. "PIV uncertainty propagation." Measurement Science and Technology 27, no. 8 (June 29, 2016): 084006. http://dx.doi.org/10.1088/0957-0233/27/8/084006.

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Hessling, J. P. "Propagation of dynamic measurement uncertainty." Measurement Science and Technology 22, no. 10 (September 2, 2011): 105105. http://dx.doi.org/10.1088/0957-0233/22/10/105105.

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Jones, Brandon A., and Ryan Weisman. "Multi-fidelity orbit uncertainty propagation." Acta Astronautica 155 (February 2019): 406–17. http://dx.doi.org/10.1016/j.actaastro.2018.10.023.

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Mezić, Igor, and Thordur Runolfsson. "Uncertainty propagation in dynamical systems." Automatica 44, no. 12 (December 2008): 3003–13. http://dx.doi.org/10.1016/j.automatica.2008.04.020.

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Pommé, S., S. M. Jerome, and C. Venchiarutti. "Uncertainty propagation in nuclear forensics." Applied Radiation and Isotopes 89 (July 2014): 58–64. http://dx.doi.org/10.1016/j.apradiso.2014.02.005.

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Klein, Randolf. "Uncertainty Propagation in (Gaussian) Convolution." Research Notes of the AAS 5, no. 2 (February 1, 2021): 39. http://dx.doi.org/10.3847/2515-5172/abe8df.

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Albert, Daniel R. "Monte Carlo Uncertainty Propagation with the NIST Uncertainty Machine." Journal of Chemical Education 97, no. 5 (April 15, 2020): 1491–94. http://dx.doi.org/10.1021/acs.jchemed.0c00096.

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PARSONS, SIMON. "FURTHER RESULTS IN QUALITATIVE UNCERTAINTY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 03, no. 02 (June 1995): 187–210. http://dx.doi.org/10.1142/s0218488595000141.

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This paper extends previous work on propagating qualitative uncertainty in networks in which a general approach to qualitative propagation was discussed. The work presented here includes results that make it possible to perform evidential and intercausal reasoning, in addition to the predictive reasoning already covered, in networks quantified with probability, possibility and Dempster-Shafer belief values. The use of these forms of reasoning, which include the phenomenon of “explaining away”, is illustrated with the use of a medical example.

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Guo, Xianpeng, Dezhi Wang, Lilun Zhang, Yongxian Wang, Wenbin Xiao, and Xinghua Cheng. "Uncertainty Quantification of Underwater Sound Propagation Loss Integrated with Kriging Surrogate Model." International Journal of Signal Processing Systems 5, no. 4 (December 2017): 141–45. http://dx.doi.org/10.18178/ijsps.5.4.141-145.

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Dissertations / Theses on the topic "Propagation of uncertainty"

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Chetwynd, Daley. "Uncertainty propagation in nonlinear systems." Thesis, University of Sheffield, 2005. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425587.

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Fiorito, Luca. "Nuclear data uncertainty propagation and uncertainty quantification in nuclear codes." Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/238375.

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Uncertainties in nuclear model responses must be quantified to define safety limits, minimize costs and define operational conditions in design. Response uncertainties can also be used to provide a feedback on the quality and reliability of parameter evaluations, such as nuclear data. The uncertainties of the predictive model responses sprout from several sources, e.g. nuclear data, model approximations, numerical solvers, influence of random variables. It was proved that the largest quantifiable sources of uncertainty in nuclear models, such as neutronics and burnup calculations, are the nuclear data, which are provided as evaluated best estimates and uncertainties/covariances in data libraries. Nuclear data uncertainties and/or covariances must be propagated to the model responses with dedicated uncertainty propagation tools. However, most of the nuclear codes for neutronics and burnup models do not have these capabilities and produce best-estimate results without uncertainties. In this work, the nuclear data uncertainty propagation was concentrated on the SCK•CEN code burnup ALEPH-2 and the Monte Carlo N-Particle code MCNP.Two sensitivity analysis procedures, i.e. FSAP and ASAP, based on linear perturbation theory were implemented in ALEPH-2. These routines can propagate nuclear data uncertainties in pure decay models. ASAP and ALEPH-2 were tested and validated against the decay heat and uncertainty quantification for several fission pulses and for the MYRRHA subcritical system. The decay uncertainty is necessary to define the reliability of the decay heat removal systems and prevent overheating and mechanical failure of the reactor components. It was proved that the propagation of independent fission yield and decay data uncertainties can be carried out with ASAP also in neutron irradiation models. Because of the ASAP limitations, the Monte Carlo sampling solver NUDUNA was used to propagate cross section covariances. The applicability constraints of ASAP drove our studies towards the development of a tool that could propagate the uncertainty of any nuclear datum. In addition, the uncertainty propagation tool was supposed to operate with multiple nuclear codes and systems, including non-linear models. The Monte Carlo sampling code SANDY was developed. SANDY is independent of the predictive model, as it only interacts with the nuclear data in input. Nuclear data are sampled from multivariate probability density functions and propagated through the model according to the Monte Carlo sampling theory. Not only can SANDY propagate nuclear data uncertainties and covariances to the model responses, but it is also able to identify the impact of each uncertainty contributor by decomposing the response variance. SANDY was extensively tested against integral parameters and was used to quantify the neutron multiplication factor uncertainty of the VENUS-F reactor.Further uncertainty propagation studies were carried out for the burnup models of light water reactor benchmarks. Our studies identified fission yields as the largest source of uncertainty for the nuclide density evolution curves of several fission products. However, the current data libraries provide evaluated fission yields and uncertainties devoid of covariance matrices. The lack of fission yield covariance information does not comply with the conservation equations that apply to a fission model, and generates inconsistency in the nuclear data. In this work, we generated fission yield covariance matrices using a generalised least-square method and a set of physical constraints. The fission yield covariance matrices solve the inconsistency in the nuclear data libraries and reduce the role of the fission yields in the uncertainty quantification of burnup models responses.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

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Kubicek, Martin. "High dimensional uncertainty propagation for hypersonic flows and entry propagation." Thesis, University of Strathclyde, 2018. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=30780.

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To solve complex design problems, engineers cannot avoid to take into account the involved uncertainties. This is important for the analysis and design of hypersonic objects and vehicles, which have to operate in extreme conditions. In this work, two approaches for a high dimensional uncertainty quantification (UQ) are developed. The first approach performs a single-fidelity non-intrusive forward UQ, while the second one performs a multi fidelity UQ, as an extension of the first approach. Both methods are focused on real engineering problems and, therefore, appropriate heuristics are included to achieve an optimal trade-off between accuracy and computational costs. In the first approach, the stochastic domain is decomposed into domains of lower dimensionality, and, then, each domain is handled separately. This is possible due to the application of the HDMR, which is here derived in a new way. This new derivation allowed to deduce important conclusions about the high dimensional modelling, which are used in the prediction scheme. This novel approach for the selection of the higher order interaction effects drastically reduce the required number of samples. In order to have optimally distributed samples for the problem of interest, the adaptive sampling scheme is introduced. Moreover, the multi-surrogate approach is introduced in order to improve the robustness of the method. The single-fidelity approach is tested on a debris re-entry case and the method is validated with respect to the MC simulation method. In the second approach, the multi-fidelity approach has been developed. In order to have the optimal combination of the low fidelity models, the power ratio approach is introduced. To correct the low fidelity model, the classical additive correction, adapted to work within the HDMR approach, is used. The multi-fidelity approach has been tested on the GOCE re-entry case, where the performed tests demonstrate the potentialities of the method.

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Malhotra, Sunil K. Caughey Thomas Kirk Caughey Thomas Kirk. "Nonlinear uncertainty propagation in space trajectories /." Diss., Pasadena, Calif. : California Institute of Technology, 1992. http://resolver.caltech.edu/CaltechETD:etd-08092007-085505.

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Becker, William. "Uncertainty propagation through large nonlinear models." Thesis, University of Sheffield, 2011. http://etheses.whiterose.ac.uk/15000/.

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Uncertainty analysis in computer models has seen a rise in interest in recent years as a result of the increased complexity of (and dependence on) computer models in the design process. A major problem however, is that the computational cost of propagating uncertainty through large nonlinear models can be prohibitive using conventional methods (such as Monte Carlo methods). A powerful solution to this problem is to use an emulator, which is a mathematical representation of the model built from a small set of model runs at specified points in input space. Such emulators are massively cheaper to run and can be used to mimic the "true" model, with the result that uncertainty analysis and sensitivity analysis can be performed for a greatly reduced computational cost. The work here investigates the use of an emulator known as a Gaussian process (GP), which is an advanced probabilistic form of regression, hitherto relatively unknown in engineering. The GP is used to perform uncertainty and sensitivity analysis on nonlinear finite element models of a human heart valve and a novel airship design. Aside from results specific to these models, it is evident that a limitation of the GP is that non-smooth model responses cannot be accurately represented. Consequently, an extension to the GP is investigated, which uses a classification and regression tree to partition the input space, such that non-smooth responses, including bifurcations, can be modelled at boundaries. This new emulator is applied to a simple nonlinear problem, then a bifurcating finite element model. The method is found to be successful, as well as actually reducing computational cost, although it is noted that bifurcations that are not axis-aligned cannot realistically be dealt with.

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Dixon, Elsbeth Clare. "Representing uncertainty in models." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.279578.

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Busby, Daniel Gilbert Michel. "Uncertainty propagation and reduction in reservoir forecasting." Thesis, University of Leicester, 2007. http://hdl.handle.net/2381/30534.

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In this work we focus on nonparametric regression techniques based on Gaussian process, considering both the frequentist and the Bayesian approach. A new sequential experimental design strategy referred to as hierarchical adaptive experimental design is proposed and tested on synthetic functions and on realistic reservoir models using a commercial oil reservoir multiphase flow simulator. Our numerical results show that the method effectively approximate the simulators output with the required approximation accuracy using an affordable number of simulator runs. Moreover, the number of simulations necessary to reach a given approximation accuracy is sensibly reduced respect to other existing experimental designs such as maximin latin hypercubes, or other classical designs used in commercial softwares.;Once an accurate emulator of the simulator output is obtained, it can be also used to calibrate the simulator model using data observed on the real physical system. This process, referred to as history matching in reservoir forecasting, is fundamental to tune input parameters and to consequently reduce output uncertainty. An approach to model calibration using Bayesian inversion is proposed in the last part of this work. Here again a hierarchical emulator is adopted. An innovative sequential design is proposed with the objective of increasing the emulator accuracy around possible history matching solutions. The excellent performances obtained on a very complicated reservoir test case, suggest the high potential of the method to solve complicated inverse problems. The proposed methodology is about to be commercialized in an industrial environment to assist reservoir engineers in uncertainty analysis and history matching.

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Doty, Austin. "Nonlinear Uncertainty Quantification, Sensitivity Analysis, and Uncertainty Propagation of a Dynamic Electrical Circuit." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1355456642.

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Damianou, Andreas. "Deep Gaussian processes and variational propagation of uncertainty." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/9968/.

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Uncertainty propagation across components of complex probabilistic models is vital for improving regularisation. Unfortunately, for many interesting models based on non-linear Gaussian processes (GPs), straightforward propagation of uncertainty is computationally and mathematically intractable. This thesis is concerned with solving this problem through developing novel variational inference approaches. From a modelling perspective, a key contribution of the thesis is the development of deep Gaussian processes (deep GPs). Deep GPs generalise several interesting GP-based models and, hence, motivate the development of uncertainty propagation techniques. In a deep GP, each layer is modelled as the output of a multivariate GP, whose inputs are governed by another GP. The resulting model is no longer a GP but, instead, can learn much more complex interactions between data. In contrast to other deep models, all the uncertainty in parameters and latent variables is marginalised out and both supervised and unsupervised learning is handled. Two important special cases of a deep GP can equivalently be seen as its building components and, historically, were developed as such. Firstly, the variational GP-LVM is concerned with propagating uncertainty in Gaussian process latent variable models. Any observed inputs (e.g. temporal) can also be used to correlate the latent space posteriors. Secondly, this thesis develops manifold relevance determination (MRD) which considers a common latent space for multiple views. An adapted variational framework allows for strong model regularisation, resulting in rich latent space representations to be learned. The developed models are also equipped with algorithms that maximise the information communicated between their different stages using uncertainty propagation, to achieve improved learning when partially observed values are present. The developed methods are demonstrated in experiments with simulated and real data. The results show that the developed variational methodologies improve practical applicability by enabling automatic capacity control in the models, even when data are scarce.

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Mantis, George C. "Quantification and propagation of disciplinary uncertainty via bayesian statistics." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/12136.

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Books on the topic "Propagation of uncertainty"

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Grigoriu, Mircea. Stochastic Systems: Uncertainty Quantification and Propagation. London: Springer London, 2012.

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Servin, Christian, and Vladik Kreinovich. Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12628-9.

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J, Gurdak Jason. Estimating prediction uncertainty from geographical information system raster processing: A user's manual for the Raster error propagation tool (REPTool). Reston, Va: U.S. Geological Survey, 2009.

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J, Gurdak Jason. Estimating prediction uncertainty from geographical information system raster processing: A user's manual for the Raster error propagation tool (REPTool). Reston, Va: U.S. Geological Survey, 2009.

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Preisig, James Calvin. Adaptive matched field processing in an uncertain propagation environment. Woods Hole, Mass: Woods Hole Oceanographic Institution, 1992.

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Grigoriu, Mircea. Stochastic Systems: Uncertainty Quantification and Propagation. Springer, 2014.

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Stochastic Systems Uncertainty Quantification And Propagation. Springer, 2012.

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David, Draper, ed. A research agenda for assessment and propagation of model uncertainty. Santa Monica, CA: Rand, 1987.

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Center, Langley Research, ed. Propagation of experimental uncertainties from the tunnel to the body coordinate system in 3-D LDV flow field studies. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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Center, Langley Research, ed. Propagation of experimental uncertainties from the tunnel to the body coordinate system in 3-D LDV flow field studies. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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Book chapters on the topic "Propagation of uncertainty"

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Astudillo, Ramón Fernandez, and Dorothea Kolossa. "Uncertainty Propagation." In Robust Speech Recognition of Uncertain or Missing Data, 35–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21317-5_3.

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Jia, Bin, and Ming Xin. "Application Uncertainty Propagation." In Grid-based Nonlinear Estimation and Its Applications, 167–216. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019. | “A science publishers book.”: CRC Press, 2019. http://dx.doi.org/10.1201/9781315193212-7.

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Gupta, S. V. "Propagation of Uncertainty." In Measurement Uncertainties, 109–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20989-5_5.

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Ratcliffe, Colin, and Bridget Ratcliffe. "Propagation of Uncertainty An Uncertainty Budget Example." In Doubt-Free Uncertainty In Measurement, 39–53. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12063-8_5.

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Franco, Andrea, Marco Correia, and Jorge Cruz. "Uncertainty Propagation in Biomedical Models." In Artificial Intelligence in Medicine, 166–71. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19551-3_21.

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San-Juan, Juan Félix, Montserrat San-Martín, Iván Pérez, Rosario López, Edna Segura, and Hans Carrillo. "Uncertainty Propagation Using Hybrid Methods." In Advances in Intelligent Systems and Computing, 709–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57802-2_68.

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Le Maître, O. P., and O. M. Knio. "Introduction: Uncertainty Quantification and Propagation." In Scientific Computation, 1–13. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3520-2_1.

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Brevault, Loïc, Mathieu Balesdent, and Jérôme Morio. "Uncertainty Propagation and Sensitivity Analysis." In Springer Optimization and Its Applications, 69–117. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39126-3_3.

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Brevault, Loïc, and Mathieu Balesdent. "Uncertainty Propagation for Multidisciplinary Problems." In Springer Optimization and Its Applications, 187–233. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39126-3_6.

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Gertsbakh, Ilya. "Measurement Uncertainty: Error Propagation Formula." In Measurement Theory for Engineers, 87–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-08583-7_5.

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Conference papers on the topic "Propagation of uncertainty"

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Ng, Leo Wai-Tsun, Dinh Bao Phuong Huynh, and Karen Willcox. "Multifidelity Uncertainty Propagation for Optimization Under Uncertainty." In 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-5602.

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Meyn, Larry. "An uncertainty propagation methodology that simplifies uncertainty analyses." In 38th Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2000. http://dx.doi.org/10.2514/6.2000-149.

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Hernando Ayuso, Javier, and Claudio Bombardelli. "Orbit Uncertainty Propagation Using Dromo." In AIAA/AAS Astrodynamics Specialist Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-5632.

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Choi, Hyungjin, Peter J. Seiler, and Sairaj V. Dhople. "Uncertainty propagation with Semidefinite Programming." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7403157.

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Rebba, Ramesh, Sankaran Mahadevan, and Ruoxue Zhang. "Validation of Uncertainty Propagation Models." In 44th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-1913.

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Hill, Jonathan, and David C. Conner. "Revisiting Uncertainty Propagation in Robotics." In SoutheastCon 2020. IEEE, 2020. http://dx.doi.org/10.1109/southeastcon44009.2020.9249741.

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Krisper, Michael, Johannes Iber, Jürgen Dobaj, and Christian Kreiner. "Patterns for Implementing Uncertainty Propagation." In EuroPLoP '18: 23rd European Conference on Pattern Languages of Programs. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3282308.3282323.

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Pourgol-Mohammad, Mohammad. "Uncertainty Propagation in Complex Codes Calculations." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-16570.

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The uncertainty propagation is an important segment of quantitative uncertainty analysis for complex computational codes (e.g., RELAP5 thermal-hydraulics) computations. Different sampling techniques, dependencies between uncertainty sources, and accurate inference on results are among the issues to be considered. The dynamic behavior of the system codes executed in each time step, results in transformation of accumulated errors and uncertainties to next time step. Depending on facility type, availability of data, scenario specification, computing machine and the software used, propagation of uncertainty results in considerably different results. This paper discusses the practical considerations of uncertainty propagation for code computations. The study evaluates the implications of the complexity on propagation of the uncertainties through inputs, sub-models and models. The study weighs different techniques of propagation, their statistics with considering their advantages and limitation at dealing with the problem. The considered methods are response surface, Monte Carlo (including simple, Latin Hypercube, and importance sampling) and boot-strap techniques. As a case study, the paper will discuss uncertainty propagation of the Integrated Methodology on Thermal-Hydraulics Uncertainty Analysis (IMTHUA). The methodology comprehensively covers various aspects of complex code uncertainty assessment for important accident transients. It explicitly examines the TH code structural uncertainties by treating internal sub-model uncertainties and by propagating such model uncertainties along with parameters in the code calculations. The two-step specification of IMTHUA (input phase following with the output updating) makes it special case to make sure that the figure of merit statistical coverage is achieved at the end with target confidence level. Tolerance limit statistics provide confidence a level on the level of coverage depending on the sample size, number of output measures, and one-sided or two-sided type of statistics. This information should be transferred to the second phase in the form of a probability distribution for each of the output measures. The research question is how to use data to develop such distributions from the corresponding tolerance limit statistics. Two approaches of using extreme values method and Bayesian updating are selected to estimate the parametric distribution parameters and compare the coverage in respect to the selected coverage criteria. The analysis is demonstrated on the large break loss of coolant accident for the LOFT test facility.

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Ciabarri, Fabio, Marco Pirrone, and Cristiano Tarchiani. "ANALYTICAL UNCERTAINTY PROPAGATION IN FACIES CLASSIFICATION WITH UNCERTAIN LOG-DATA." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0071.

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Log-facies classification aims to predict a vertical profile of facies at well location with log readings or rock properties calculated in the formation evaluation and/or rock-physics modeling analysis as input. Various classification approaches are described in the literature and new ones continue to appear based on emerging Machine Learning techniques. However, most of the available classification methods assume that the inputs are accurate and their inherent uncertainty, related to measurement errors and interpretation steps, is usually neglected. Accounting for facies uncertainty is not a mere exercise in style, rather it is fundamental for the purpose of understanding the reliability of the classification results, and it also represents a critical information for 3D reservoir modeling and/or seismic characterization processes. This is particularly true in wells characterized by high vertical heterogeneity of rock properties or thinly bedded stratigraphy. Among classification methods, probabilistic classifiers, which relies on the principle of Bayes decision theory, offer an intuitive way to model and propagate measurements/rock properties uncertainty into the classification process. In this work, the Bayesian classifier is enhanced such that the most likely classification of facies is expressed by maximizing the integral product between three probability functions. The latters describe: (1) the a-priori information on facies proportion (2) the likelihood of a set of measurements/rock properties to belong to a certain facies-class and (3) the uncertainty of the inputs to the classifier (log data or rock properties derived from them). Reliability of the classification outcome is therefore improved by accounting for both the global uncertainty, related to facies classes overlap in the classification model, and the depth-dependent uncertainty related to log data. As derived in this work, the most interesting feature of the proposed formulation, although generally valid for any type of probability functions, is that it can be analytically solved by representing the input distributions as a Gaussian mixture model and their related uncertainty as an additive white Gaussian noise. This gives a robust, straightforward and fast approach that can be effortlessly integrated in existing classification workflows. The proposed classifier is tested in various well-log characterization studies on clastic depositional environments where Monte-Carlo realizations of rock properties curves, output of a statistical formation evaluation analysis, are used to infer rock properties distributions. Uncertainty on rock properties, modeled as an additive white Gaussian noise, are then statistically estimated (independently at each depth along the well profile) from the ensemble of Monte-Carlo realizations. At the same time, a classifier, based on a Gaussian mixture model, is parametrically inferred from the pointwise mean of the Monte Carlo realizations given an a-priori reference profile of facies. Classification results, given by the a-posteriori facies proportion and the maximum a-posteriori prediction profiles, are finally computed. The classification outcomes clearly highlight that neglecting uncertainty leads to an erroneous final interpretation, especially at the transition zone between different facies. As mentioned, this become particularly remarkable in complex environments and highly heterogeneous scenarios.

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Jiang, Zhen, Wei Li, Daniel W. Apley, and Wei Chen. "A System Uncertainty Propagation Approach With Model Uncertainty Quantification in Multidisciplinary Design." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34708.

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The performance of a multidisciplinary system is inevitably affected by various sources of uncertainties, usually categorized as aleatory (e.g. input variability) or epistemic (e.g. model uncertainty) uncertainty. In the framework of design under uncertainty, all sources of uncertainties should be aggregated to assess the uncertainty of system quantities of interest (QOIs). In a multidisciplinary design system, uncertainty propagation refers to the analysis that quantifies the overall uncertainty of system QOIs resulting from all sources of aleatory and epistemic uncertainty originating in the individual disciplines. However, due to the complexity of multidisciplinary simulation, especially the coupling relationships between individual disciplines, many uncertainty propagation approaches in the existing literature only consider aleatory uncertainty and ignore the impact of epistemic uncertainty. In this paper, we address the issue of efficient uncertainty quantification of system QOIs considering both aleatory and epistemic uncertainties. We propose a spatial-random-process (SRP) based multidisciplinary uncertainty analysis (MUA) method that, subsequent to SRP-based disciplinary model uncertainty quantification, fully utilizes the structure of SRP emulators and leads to compact analytical formulas for assessing statistical moments of uncertain QOIs. The proposed method is applied to a benchmark electronics packaging problem. To demonstrate the effectiveness of the method, the estimated low-order statistical moments of the QOIs are compared to the results from Monte Carlo simulations.

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Reports on the topic "Propagation of uncertainty"

1

Geist, William H. Basic Statistics and Uncertainty Propagation. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1244325.

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2

Jacobs, D. W., and T. D. Alter. Uncertainty Propagation in Model-Based Recognition. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada295642.

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3

Banks, H. T., and Shuhua Hu. Propagation of Uncertainty in Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada556937.

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4

Holland, Charles W. Predicting the Impact of Seabed Uncertainty and Variability on Propagation Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533135.

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5

Holland, Charles W. Predicting the Impact of Seabed Uncertainty and Variability on Propagation Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada542042.

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Holland, Charles W. Predicting the Impact of Seabed Uncertainty and Variability on Propagation Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada564659.

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Holland, Charles W. Predicting the Impact of Seabed Uncertainty and Variability on Propagation Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada571376.

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8

Holland, Troy Michael, Joel David Kress, and Kabekode Ghanasham Bhat. Calibration and Propagation of Uncertainty for Independence. Office of Scientific and Technical Information (OSTI), June 2017. http://dx.doi.org/10.2172/1367827.

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9

Porter, Michael B., and Paul Hursky. Modeling and Analysing the Propagation of Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada628081.

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Porter, Michael B., Paul Hursky, and T. M. Siderius. Modeling and Analyzing the Propagation of Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada630036.

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