Literatura científica selecionada sobre o tema "Uncertainly quantification"
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Artigos de revistas sobre o assunto "Uncertainly quantification"
Jalaian, Brian, Michael Lee e Stephen Russell. "Uncertain Context: Uncertainty Quantification in Machine Learning". AI Magazine 40, n.º 4 (20 de dezembro de 2019): 40–49. http://dx.doi.org/10.1609/aimag.v40i4.4812.
Texto completo da fonteVerdonck, H., O. Hach, J. D. Polman, O. Braun, C. Balzani, S. Müller e J. Rieke. "-An open-source framework for the uncertainty quantification of aeroelastic wind turbine simulation tools". Journal of Physics: Conference Series 2265, n.º 4 (1 de maio de 2022): 042039. http://dx.doi.org/10.1088/1742-6596/2265/4/042039.
Texto completo da fonteCacuci, Dan Gabriel. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems". Energies 15, n.º 17 (1 de setembro de 2022): 6379. http://dx.doi.org/10.3390/en15176379.
Texto completo da fonteOh, Deog Yeon, Young Seok Bang, Kwang Won Seul e Sweng Woong Woo. "ICONE23-1466 UNCERTAINTY QUANTIFICATION OF PHYSICAL MODELS USING CIRCE METHOD". Proceedings of the International Conference on Nuclear Engineering (ICONE) 2015.23 (2015): _ICONE23–1—_ICONE23–1. http://dx.doi.org/10.1299/jsmeicone.2015.23._icone23-1_213.
Texto completo da fonteHu, Juxi, Lei Wang e Xiaojun Wang. "Non-Probabilistic Uncertainty Quantification of Fiber-Reinforced Composite Laminate Based on Micro- and Macro-Mechanical Analysis". Applied Sciences 12, n.º 22 (18 de novembro de 2022): 11739. http://dx.doi.org/10.3390/app122211739.
Texto completo da fonteSun, X., T. Kirchdoerfer e M. Ortiz. "Rigorous uncertainty quantification and design with uncertain material models". International Journal of Impact Engineering 136 (fevereiro de 2020): 103418. http://dx.doi.org/10.1016/j.ijimpeng.2019.103418.
Texto completo da fonteCheng, Xi, Clément Henry, Francesco P. Andriulli, Christian Person e Joe Wiart. "A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data". International Journal of Environmental Research and Public Health 17, n.º 7 (9 de abril de 2020): 2586. http://dx.doi.org/10.3390/ijerph17072586.
Texto completo da fonteErnst, Oliver, Fabio Nobile, Claudia Schillings e Tim Sullivan. "Uncertainty Quantification". Oberwolfach Reports 16, n.º 1 (26 de fevereiro de 2020): 695–772. http://dx.doi.org/10.4171/owr/2019/12.
Texto completo da fonteSalehghaffari, S., e M. Rais-Rohani. "Material model uncertainty quantification using evidence theory". Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, n.º 10 (8 de janeiro de 2013): 2165–81. http://dx.doi.org/10.1177/0954406212473390.
Texto completo da fonteTuczyński, Tomasz, e Jerzy Stopa. "Uncertainty Quantification in Reservoir Simulation Using Modern Data Assimilation Algorithm". Energies 16, n.º 3 (20 de janeiro de 2023): 1153. http://dx.doi.org/10.3390/en16031153.
Texto completo da fonteTeses / dissertações sobre o assunto "Uncertainly quantification"
Nguyen, Trieu Nhat Thanh. "Modélisation et simulation d'éléments finis du système pelvien humain vers un outil d'aide à la décision fiable : incertitude des données et des lois de comportement". Electronic Thesis or Diss., Centrale Lille Institut, 2024. http://www.theses.fr/2024CLIL0015.
Texto completo da fonteApproximately 0.5 million deaths during childbirth occur annually, as reported by the World Health Organization (WHO). One prominent cause is complicated obstructed labor, also known as labor dystocia. This condition arises when the baby fails to navigate the birth canal despite normal uterine contractions. Therefore, understanding this complex physiological process is essential for improving diagnosis, optimizing clinical interventions, and defining predictive and preventive strategies. Currently, due to the complexity of experimental protocols and associated ethical issues, computational modeling and simulation of childbirth have emerged as the most promising solutions to achieve these objectives. However, it is crucial to quantify the significant influence of inherent uncertainties in the parameters and behaviors of the human pelvic system and their propagation through simulations to establish reliable indicators for clinical decision-making. Specifically, epistemic uncertainties due to lack of knowledge and aleatoric uncertainties due to intrinsic variability in physical domain geometries, material properties, and loads are often not fully understood and are frequently overlooked in current literature on childbirth computational modeling and simulation.This PhD thesis addresses three original contributions aimed at overcoming these challenges: 1) development and evaluation of a computational workflow for the uncertainty quantification of hyperelastic properties of the soft tissue using precise and imprecise probabilities; 2) extrapolation of the developed protocol for the uncertainty quantification of the active uterine contraction during the second stage of labor simulation; and 3) development and evaluation of a fetus descent simulation with the active uterine contraction using MRI-based observations and associated uncertainty quantification process.This thesis pays the way to a more reliable childbirth modeling and simulation under passive and active uterine contractions. In fact, the developed computational protocols could be extrapolated into a patient-specific modeling and simulation to identify the risk factors and associated strategies for vaginal delivery complications in a straightforward manner. Finally, the investigation of stochastic finite element formulation will allow to improve the computational cost for the uncertainty quantification process
Elfverson, Daniel. "Multiscale Methods and Uncertainty Quantification". Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-262354.
Texto completo da fonteParkinson, Matthew. "Uncertainty quantification in Radiative Transport". Thesis, University of Bath, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767610.
Texto completo da fonteCarson, J. "Uncertainty quantification in palaeoclimate reconstruction". Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/29076/.
Texto completo da fonteBoopathy, Komahan. "Uncertainty Quantification and Optimization Under Uncertainty Using Surrogate Models". University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398302731.
Texto completo da fonteKalmikov, Alexander G. "Uncertainty Quantification in ocean state estimation". Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79291.
Texto completo da fonteCataloged from PDF version of thesis.
Includes bibliographical references (p. 158-160).
Quantifying uncertainty and error bounds is a key outstanding challenge in ocean state estimation and climate research. It is particularly difficult due to the large dimensionality of this nonlinear estimation problem and the number of uncertain variables involved. The "Estimating the Circulation and Climate of the Oceans" (ECCO) consortium has developed a scalable system for dynamically consistent estimation of global time-evolving ocean state by optimal combination of ocean general circulation model (GCM) with diverse ocean observations. The estimation system is based on the "adjoint method" solution of an unconstrained least-squares optimization problem formulated with the method of Lagrange multipliers for fitting the dynamical ocean model to observations. The dynamical consistency requirement of ocean state estimation necessitates this approach over sequential data assimilation and reanalysis smoothing techniques. In addition, it is computationally advantageous because calculation and storage of large covariance matrices is not required. However, this is also a drawback of the adjoint method, which lacks a native formalism for error propagation and quantification of assimilated uncertainty. The objective of this dissertation is to resolve that limitation by developing a feasible computational methodology for uncertainty analysis in dynamically consistent state estimation, applicable to the large dimensionality of global ocean models. Hessian (second derivative-based) methodology is developed for Uncertainty Quantification (UQ) in large-scale ocean state estimation, extending the gradient-based adjoint method to employ the second order geometry information of the model-data misfit function in a high-dimensional control space. Large error covariance matrices are evaluated by inverting the Hessian matrix with the developed scalable matrix-free numerical linear algebra algorithms. Hessian-vector product and Jacobian derivative codes of the MIT general circulation model (MITgcm) are generated by means of algorithmic differentiation (AD). Computational complexity of the Hessian code is reduced by tangent linear differentiation of the adjoint code, which preserves the speedup of adjoint checkpointing schemes in the second derivative calculation. A Lanczos algorithm is applied for extracting the leading rank eigenvectors and eigenvalues of the Hessian matrix. The eigenvectors represent the constrained uncertainty patterns. The inverse eigenvalues are the corresponding uncertainties. The dimensionality of UQ calculations is reduced by eliminating the uncertainty null-space unconstrained by the supplied observations. Inverse and forward uncertainty propagation schemes are designed for assimilating observation and control variable uncertainties, and for projecting these uncertainties onto oceanographic target quantities. Two versions of these schemes are developed: one evaluates reduction of prior uncertainties, while another does not require prior assumptions. The analysis of uncertainty propagation in the ocean model is time-resolving. It captures the dynamics of uncertainty evolution and reveals transient and stationary uncertainty regimes. The system is applied to quantifying uncertainties of Antarctic Circumpolar Current (ACC) transport in a global barotropic configuration of the MITgcm. The model is constrained by synthetic observations of sea surface height and velocities. The control space consists of two-dimensional maps of initial and boundary conditions and model parameters. The size of the Hessian matrix is 0(1010) elements, which would require 0(60GB) of uncompressed storage. It is demonstrated how the choice of observations and their geographic coverage determines the reduction in uncertainties of the estimated transport. The system also yields information on how well the control fields are constrained by the observations. The effects of controls uncertainty reduction due to decrease of diagonal covariance terms are compared to dynamical coupling of controls through off-diagonal covariance terms. The correlations of controls introduced by observation uncertainty assimilation are found to dominate the reduction of uncertainty of transport. An idealized analytical model of ACC guides a detailed time-resolving understanding of uncertainty dynamics. Keywords: Adjoint model uncertainty, sensitivity, posterior error reduction, reduced rank Hessian matrix, Automatic Differentiation, ocean state estimation, barotropic model, Drake Passage transport.
by Alexander G. Kalmikov.
Ph.D.
Malenova, Gabriela. "Uncertainty quantification for high frequency waves". Licentiate thesis, KTH, Numerisk analys, NA, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186287.
Texto completo da fonteQC 20160510
Roy, Pamphile. "Uncertainty quantification in high dimensional problems". Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0038.
Texto completo da fonteUncertainties are predominant in the world that we know. Referring therefore to a nominal value is too restrictive, especially when it comes to complex systems. Understanding the nature and the impact of these uncertainties has become an important aspect of engineering work. On a societal point of view, uncertainties play a role in terms of decision-making. From the European Commission through the Better Regulation Guideline, impact assessments are now advised to take uncertainties into account. In order to understand the uncertainties, the mathematical field of uncertainty quantification has been formed. UQ encompasses a large palette of statistical tools and it seeks to link a set of input perturbations on a system (design of experiments) towards a quantity of interest. The purpose of this work is to propose improvements on various methodological aspects of uncertainty quantification applied to costly numerical simulations. This is achieved by using existing methods with a multi-strategy approach but also by creating new methods. In this context, novel sampling and resampling approaches have been developed to better capture the variability of the physical phenomenon when dealing with a high number of perturbed inputs. These allow to reduce the number of simulation required to describe the system. Moreover, novel methods are proposed to visualize uncertainties when dealing with either a high dimensional input parameter space or a high dimensional quantity of interest. The developed methods can be used in various fields like hydraulic modelling and aerodynamic modelling. Their capabilities are demonstrated in realistic systems using well established computational fluid dynamics tools. Lastly, they are not limited to the use of numerical experiments and can be used equally for real experiments
Alvarado, Martin Guillermo. "Quantification of uncertainty during history matching". Texas A&M University, 2003. http://hdl.handle.net/1969/463.
Texto completo da fonteJimenez, Edwin. "Uncertainty quantification of nonlinear stochastic phenomena". Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-11092009-161351/.
Texto completo da fonteAdvisor: M.Y. Hussaini, Florida State University, College of Arts and Sciences, Dept. of Mathematics. Title and description from dissertation home page (viewed on Mar. 16, 2010). Document formatted into pages; contains xii, 113 pages. Includes bibliographical references.
Livros sobre o assunto "Uncertainly quantification"
Soize, Christian. Uncertainty Quantification. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0.
Texto completo da fonteSullivan, T. J. Introduction to Uncertainty Quantification. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23395-6.
Texto completo da fonteGhanem, Roger, David Higdon e Houman Owhadi, eds. Handbook of Uncertainty Quantification. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-11259-6.
Texto completo da fonteSouza de Cursi, Eduardo. Uncertainty Quantification using R. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17785-9.
Texto completo da fonteSouza de Cursi, Eduardo. Uncertainty Quantification with R. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-48208-3.
Texto completo da fonteLe Maître, O. P., e Omar M. Knio. Spectral Methods for Uncertainty Quantification. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-3520-2.
Texto completo da fonteDienstfrey, Andrew M., e Ronald F. Boisvert, eds. Uncertainty Quantification in Scientific Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32677-6.
Texto completo da fonteMcClarren, Ryan G. Uncertainty Quantification and Predictive Computational Science. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0.
Texto completo da fonteBijl, Hester, Didier Lucor, Siddhartha Mishra e Christoph Schwab, eds. Uncertainty Quantification in Computational Fluid Dynamics. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00885-1.
Texto completo da fonteBardsley, Johnathan M. Computational Uncertainty Quantification for Inverse Problems. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975383.
Texto completo da fonteCapítulos de livros sobre o assunto "Uncertainly quantification"
Soize, Christian. "Fundamental Notions in Stochastic Modeling of Uncertainties and Their Propagation in Computational Models". In Uncertainty Quantification, 1–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_1.
Texto completo da fonteSoize, Christian. "Random Fields and Uncertainty Quantification in Solid Mechanics of Continuum Media". In Uncertainty Quantification, 245–300. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_10.
Texto completo da fonteSoize, Christian. "Elements of Probability Theory". In Uncertainty Quantification, 17–40. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_2.
Texto completo da fonteSoize, Christian. "Markov Process and Stochastic Differential Equation". In Uncertainty Quantification, 41–59. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_3.
Texto completo da fonteSoize, Christian. "MCMC Methods for Generating Realizations and for Estimating the Mathematical Expectation of Nonlinear Mappings of Random Vectors". In Uncertainty Quantification, 61–76. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_4.
Texto completo da fonteSoize, Christian. "Fundamental Probabilistic Tools for Stochastic Modeling of Uncertainties". In Uncertainty Quantification, 77–132. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_5.
Texto completo da fonteSoize, Christian. "Brief Overview of Stochastic Solvers for the Propagation of Uncertainties". In Uncertainty Quantification, 133–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_6.
Texto completo da fonteSoize, Christian. "Fundamental Tools for Statistical Inverse Problems". In Uncertainty Quantification, 141–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_7.
Texto completo da fonteSoize, Christian. "Uncertainty Quantification in Computational Structural Dynamics and Vibroacoustics". In Uncertainty Quantification, 155–216. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_8.
Texto completo da fonteSoize, Christian. "Robust Analysis with Respect to the Uncertainties for Analysis, Updating, Optimization, and Design". In Uncertainty Quantification, 217–43. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_9.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Uncertainly quantification"
Misaka, Takashi, Shigeru Obayashi e Shinkyu Jeong. "Uncertainly Quantification of Lidar-Derived Wake Vortex Parameters with/without Data Assimilation (Invited)". In 8th AIAA Atmospheric and Space Environments Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3271.
Texto completo da fonteZhang, Qian, Shenren Xu, Xianjun Yu, Jiaxin Liu, Dingxi Wang e Xiuquan Huang. "Quantification of Compressor Aerodynamic Performance Deviation due to Manufacturing Uncertainty Using the Adjoint Method". In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-59.
Texto completo da fonteLee, Nian-Ze, Yen-Shi Wang e Jie-Hong R. Jiang. "Solving Stochastic Boolean Satisfiability under Random-Exist Quantification". In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/96.
Texto completo da fonteKotteda, V. M. Krushnarao, Anitha Kommu, Vinod Kumar e William Spotz. "Uncertainty Quantification of a Fluidized Bed Reactor". In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-4844.
Texto completo da fonteJayaraman, Buvana, Manas Khurana, Andrew Wissink e Rohit Jain. "Uncertainty Quantification Approach for Rotorcraft Simulations". In Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17462.
Texto completo da fonteBudzien, Joanne, James Byerly, Rob Aulwes, Rao Garimella, Angela Herring e Jon Woodring. "Linking Material Models Between Codes: Establishing Thermodynamic Consistency". In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86808.
Texto completo da fonteEça, L., K. Dowding e P. J. Roache. "On the Application of the Area Metric to Validation Exercises of Stochastic Simulations". In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86809.
Texto completo da fonteDavis, Brad, Gregory Langone e Nicholas Reisweber. "Sensitivity Analysis and Bayesian Calibration of a Holmquist-Johnson-Cook Material Model for Cellular Concrete Subjected to Impact Loading". In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86800.
Texto completo da fonte"VVUQ2022 Front Matter". In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-fm1.
Texto completo da fonteKirsch, Jared, Nima Fathi e Joshua Hubbard. "Validation Analysis of Medium-Scale Methanol Pool Fire". In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86806.
Texto completo da fonteRelatórios de organizações sobre o assunto "Uncertainly quantification"
Caldeira, Joao. Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms. Office of Scientific and Technical Information (OSTI), abril de 2020. http://dx.doi.org/10.2172/1623354.
Texto completo da fonteUrban, Nathan Mark. Climate Uncertainty Quantification at LANL. Office of Scientific and Technical Information (OSTI), abril de 2016. http://dx.doi.org/10.2172/1250690.
Texto completo da fonteThiagarajan, J. Uncertainty Quantification in Scientific ML. Office of Scientific and Technical Information (OSTI), setembro de 2020. http://dx.doi.org/10.2172/1670557.
Texto completo da fonteStracuzzi, David, Maximillian Chen, Michael Darling, Matthew Peterson e Charlie Vollmer. Uncertainty Quantification for Machine Learning. Office of Scientific and Technical Information (OSTI), junho de 2017. http://dx.doi.org/10.2172/1733262.
Texto completo da fonteKarpius, Peter. Nuclide Identification, Quantification, and Uncertainty. Office of Scientific and Technical Information (OSTI), maio de 2021. http://dx.doi.org/10.2172/1782632.
Texto completo da fonteCroft, Stephen, e Andrew Nicholson. OR14-V-Uncertainty-PD2La Uncertainty Quantification Workshop Report. Office of Scientific and Technical Information (OSTI), julho de 2017. http://dx.doi.org/10.2172/1784220.
Texto completo da fonteSeifried, Jeffrey E. Adjoint-Based Uncertainty Quantification with MCNP. Office of Scientific and Technical Information (OSTI), setembro de 2011. http://dx.doi.org/10.2172/1110395.
Texto completo da fonteSrinivasan, Gowri. Need for Uncertainty Quantification in Predictions. Office of Scientific and Technical Information (OSTI), julho de 2015. http://dx.doi.org/10.2172/1191117.
Texto completo da fonteDe Bord, Sarah. Tutorial examples for uncertainty quantification methods. Office of Scientific and Technical Information (OSTI), agosto de 2015. http://dx.doi.org/10.2172/1213490.
Texto completo da fonteWilliams, Mark L. Whitepaper on Uncertainty Quantification for MPACT. Office of Scientific and Technical Information (OSTI), dezembro de 2015. http://dx.doi.org/10.2172/1255677.
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