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Rozprawy doktorskie na temat „Uncertainly quantification”

Autor: Grafiati

Data publikacji: 14 grudnia 2024

Data aktualizacji: 31 lipca 2025

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1

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.

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Cette thèse a développé une approche originale pour quantifier les incertitudes liées aux propriétés hyperélastiques des tissus mous, en utilisant à la fois des probabilités précises et imprécises. Le protocole de calcul a été étendu pour quantifier les incertitudes dans les contractions utérines actives lors des simulations du deuxième stade du travail. De plus, une simulation de la descente foetale a été créée, intégrant des données de contraction utérine active basées sur l'IRM et une quantification d'incertitude associée. L'étude a révélé que l'Expansion du Chaos Polynomial (PCE) non intru

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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.

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In this thesis we consider two great challenges in computer simulations of partial differential equations: multiscale data, varying over multiple scales in space and time, and data uncertainty, due to lack of or inexact measurements. We develop a multiscale method based on a coarse scale correction, using localized fine scale computations. We prove that the error in the solution produced by the multiscale method decays independently of the fine scale variation in the data or the computational domain. We consider the following aspects of multiscale methods: continuous and discontinuous underlyi

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Parkinson, Matthew. "Uncertainty quantification in Radiative Transport." Thesis, University of Bath, 2019. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.767610.

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We study how uncertainty in the input data of the Radiative Transport equation (RTE), affects the distribution of (functionals of) its solution (the output data). The RTE is an integro-differential equation, in up to seven independent variables, that models the behaviour of rarefied particles (such as photons and neutrons) in a domain. Its applications include nuclear reactor design, radiation shielding, medical imaging, optical tomography and astrophysics. We focus on the RTE in the context of nuclear reactor physics where, to design and maintain safe reactors, understanding the effects of un

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Carson, J. "Uncertainty quantification in palaeoclimate reconstruction." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/29076/.

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Studying the dynamics of the palaeoclimate is a challenging problem. Part of the challenge lies in the fact that our understanding must be based on only a single realisation of the climate system. With only one climate history, it is essential that palaeoclimate data are used to their full extent, and that uncertainties arising from both data and modelling are well characterised. This is the motivation behind this thesis, which explores approaches for uncertainty quantification in problems related to palaeoclimate reconstruction. We focus on uncertainty quantification problems for the glacial-

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5

Boopathy, Komahan. "Uncertainty Quantification and Optimization Under Uncertainty Using Surrogate Models." University of Dayton / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1398302731.

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Kalmikov, Alexander G. "Uncertainty Quantification in ocean state estimation." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79291.

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Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 158-160).<br>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 Circul

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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.

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We consider high frequency waves satisfying the scalar wave equationwith highly oscillatory initial data. The speed of propagation of the mediumas well as the phase and amplitude of the initial data is assumed to beuncertain, described by a finite number of independent random variables withknown probability distributions. We introduce quantities of interest (QoIs)aslocal averages of the squared modulus of the wave solution, or itsderivatives.The regularity of these QoIs in terms of the input random parameters and thewavelength is important for uncertainty quantification methods based oninterpo

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Roy, Pamphile. "Uncertainty quantification in high dimensional problems." Thesis, Toulouse, INPT, 2019. http://www.theses.fr/2019INPT0038.

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Les incertitudes font partie du monde qui nous entoure. Se limiter à une seule valeur nominale est bien souvent trop restrictif, et ce d'autant plus lorsqu'il est question de systèmes complexes. Comprendre la nature et l'impact de ces incertitudes est devenu un aspect important de tout travail d'ingénierie. D'un point de vue sociétal, les incertitudes jouent un rôle important dans les processus de décision. Les dernières recommandations de la Commission européenne en matière d'analyses des risques souligne l'importance du traitement des incertitudes. Afin de comprendre les incertitudes, une no

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9

Alvarado, Martin Guillermo. "Quantification of uncertainty during history matching." Texas A&M University, 2003. http://hdl.handle.net/1969/463.

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Jimenez, Edwin. "Uncertainty quantification of nonlinear stochastic phenomena." Tallahassee, Florida : Florida State University, 2009. http://etd.lib.fsu.edu/theses/available/etd-11092009-161351/.

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Thesis (Ph. D.)--Florida State University, 2009.<br>Advisor: 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.

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11

Timmins, Benjamin H. "Automatic Particle Image Velocimetry Uncertainty Quantification." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/884.

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The uncertainty of any measurement is the interval in which one believes the actual error lies. Particle Image Velocimetry (PIV) measurement error depends on the PIV algorithm used, a wide range of user inputs, flow characteristics, and the experimental setup. Since these factors vary in time and space, they lead to nonuniform error throughout the flow field. As such, a universal PIV uncertainty estimate is not adequate and can be misleading. This is of particular interest when PIV data are used for comparison with computational or experimental data. A method to estimate the uncertainty d

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12

Yu, Xuanlong. "Uncertainty quantification for vision regression tasks." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG094.

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Ce travail se concentre sur la quantification de l'incertitude pour les réseaux de neurones profonds, qui est vitale pour la fiabilité et la précision de l'apprentissage profond. Cependant, la conception complexe du réseau et les données d'entrée limitées rendent difficile l'estimation des incertitudes. Parallèlement, la quantification de l'incertitude pour les tâches de régression a reçu moins d'attention que pour celles de classification en raison de la sortie standardisée plus simple de ces dernières et de leur grande importance. Cependant, des problèmes de régression sont rencontrés dans u

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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 nucl

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14

Cheng, Haiyan. "Uncertainty Quantification and Uncertainty Reduction Techniques for Large-scale Simulations." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28444.

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Modeling and simulations of large-scale systems are used extensively to not only better understand a natural phenomenon, but also to predict future events. Accurate model results are critical for design optimization and policy making. They can be used effectively to reduce the impact of a natural disaster or even prevent it from happening. In reality, model predictions are often affected by uncertainties in input data and model parameters, and by incomplete knowledge of the underlying physics. A deterministic simulation assumes one set of input conditions, and generates one result without cons

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15

Cousins, William Bryan. "Boundary Conditions and Uncertainty Quantification for Hemodynamics." Thesis, North Carolina State University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3575896.

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<p> We address outflow boundary conditions for blood flow modeling. In particular, we consider a variety of fundamental issues in the structured tree boundary condition. We provide a theoretical analysis of the numerical implementation of the structured tree, showing that it is sensible but must be performed with great care. We also perform analytical and numerical studies on the sensitivity of model output on the structured tree's defining geometrical parameters. The most important component of this dissertation is the derivation of the new, generalized structured tree boundary condition. Unl

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16

Teckentrup, Aretha Leonore. "Multilevel Monte Carlo methods and uncertainty quantification." Thesis, University of Bath, 2013. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.577753.

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We consider the application of multilevel Monte Carlo methods to elliptic partial differential equations with random coefficients. Such equations arise, for example, in stochastic groundwater ow modelling. Models for random coefficients frequently used in these applications, such as log-normal random fields with exponential covariance, lack uniform coercivity and boundedness with respect to the random parameter and have only limited spatial regularity. To give a rigorous bound on the cost of the multilevel Monte Carlo estimator to reach a desired accuracy, one needs to quantify the bias of the

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17

Strandberg, Rickard, and Johan Låås. "Uncertainty quantification using high-dimensional numerical integration." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-195701.

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We consider quantities that are uncertain because they depend on one or many uncertain parameters. If the uncertain parameters are stochastic the expected value of the quantity can be obtained by integrating the quantity over all the possible values these parameters can take and dividing the result by the volume of the parameter-space. Each additional uncertain parameter has to be integrated over; if the parameters are many, this give rise to high-dimensional integrals. This report offers an overview of the theory underpinning four numerical methods used to compute high-dimensional integrals:

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18

Fadikar, Arindam. "Stochastic Computer Model Calibration and Uncertainty Quantification." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91985.

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This dissertation presents novel methodologies in the field of stochastic computer model calibration and uncertainty quantification. Simulation models are widely used in studying physical systems, which are often represented by a set of mathematical equations. Inference on true physical system (unobserved or partially observed) is drawn based on the observations from corresponding computer simulation model. These computer models are calibrated based on limited ground truth observations in order produce realistic predictions and associated uncertainties. Stochastic computer model differs from t

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19

Hagues, Andrew W. "Uncertainty quantification for problems in radionuclide transport." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/9088.

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The field of radionuclide transport has long recognised the stochastic nature of the problems encountered. Many parameters that are used in computational models are very difficult, if not impossible, to measure with any great degree of confidence. For example, bedrock properties can only be measured at a few discrete points, the properties between these points may be inferred or estimated using experiments but it is difficult to achieve any high levels of confidence. This is a major problem when many countries around the world are considering deep geologic repositories as a disposal option for

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El-Shanawany, Ashraf Ben Mamdouh. "Quantification of uncertainty in probabilistic safety analysis." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/48104.

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This thesis develops methods for quantification and interpretation of uncertainty in probabilistic safety analysis, focussing on fault trees. The output of a fault tree analysis is, usually, the probability of occurrence of an undesirable event (top event) calculated using the failure probabilities of identified basic events. The standard method for evaluating the uncertainty distribution is by Monte Carlo simulation, but this is a computationally intensive approach to uncertainty estimation and does not, readily, reveal the dominant reasons for the uncertainty. A closed form approximation for

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VIRDIS, IRENE. "Uncertainty Quantification and Optimization of Aeronautical Components." Doctoral thesis, Università degli Studi di Cagliari, 2022. http://hdl.handle.net/11584/332668.

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This project was part of a wider investigation performed on a set of 200 High Pressure Turbine (HPT) blades, dismounted after several hours of flight and characterized by in-service and manufacturing variations. The main objective of this project was to determine the impact of these variations on the aerodynamic performance of the rotor and to devise a strategy to design more robust geometries, i.e, less sensitive to the given uncertainties. The initial set of data consisted of the digitized versions of the blades (GOM scans). The geometrical deviations characterizing the blades from their h

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22

Larvaron, Benjamin. "Modeling battery health degradation with uncertainty quantification." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0028.

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Face au changement climatique, des mesures importantes doivent être prise pour décarboner l’économie. Cela inclus une transformation des secteurs du transport et de la production d’énergie. Ces transformations augmentent l’utilisation d’énergie électrique et posent la question du stockage notamment grâce aux batteries Lithium-ion. Dans cette thèse nous nous intéressons à la modélisation de la dégradation de la santé des batteries. Afin de quantifier les risques associés aux garantis de performance, les incertitudes doivent être prise en compte. La dégradation est un phénomène complexe mettant

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Lam, Xuan-Binh. "Uncertainty quantification for stochastic subspace indentification methods." Rennes 1, 2011. http://www.theses.fr/2011REN1S133.

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In Operational Modal Analysis, the modal parameters (natural frequencies, damping ratios, and mode shapes) obtained from Stochastic Subspace Identification (SSI) of a structure, are afflicted with statistical uncertainty. For evaluating the quality of the obtained results it is essential to know the appropriate uncertainty bounds of these terms. In this thesis, the algorithms, that automatically compute the uncertainty bounds of modal parameters obtained from SSI of a structure based on vibration measurements, are presented. With these new algorithms, the uncertainty bounds of the modal parame

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Liang, Yue, Tian-Chyi Jim Yeh, Yu-Li Wang, Mingwei Liu, Junjie Wang, and Yonghong Hao. "Numerical simulation of backward erosion piping in heterogeneous fields." AMER GEOPHYSICAL UNION, 2017. http://hdl.handle.net/10150/624364.

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Backward erosion piping (BEP) is one of the major causes of seepage failures in levees. Seepage fields dictate the BEP behaviors and are influenced by the heterogeneity of soil properties. To investigate the effects of the heterogeneity on the seepage failures, we develop a numerical algorithm and conduct simulations to study BEP progressions in geologic media with spatially stochastic parameters. Specifically, the void ratio e, the hydraulic conductivity k, and the ratio of the particle contents r of the media are represented as the stochastic variables. They are characterized by means and va

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Ndiaye, Aïssatou. "Uncertainty Quantification of Thermo-acousticinstabilities in gas turbine combustors." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS062/document.

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Les instabilités thermo-acoustiques résultent de l'interaction entre les oscillations de pression acoustique et les fluctuations du taux de dégagement de chaleur de la flamme. Ces instabilités de combustion sont particulièrement préoccupantes en raison de leur fréquence dans les turbines à gaz modernes et à faible émission. Leurs principaux effets indésirables sont une réduction du temps de fonctionnement du moteur en raison des oscillations de grandes amplitudes ainsi que de fortes vibrations à l'intérieur de la chambre de combustion. La simulation numérique est maintenant devenue une approch

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au, P. Kraipeerapun@murdoch edu, and Pawalai Kraipeerapun. "Neural network classification based on quantification of uncertainty." Murdoch University, 2009. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20090526.100525.

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This thesis deals with feedforward backpropagation neural networks and interval neutrosophic sets for the binary and multiclass classification problems. Neural networks are used to predict true and false output values. These results together with the uncertainty of type error and vagueness occurred in the prediction are then represented in the form of interval neutrosophic sets. Each element in an interval neutrosophic set consists of three membership values: truth, indeterminacy, and false. These three membership values are then used in the classification process. For binary classificatio

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27

Pettersson, Per. "Uncertainty Quantification and Numerical Methods for Conservation Laws." Doctoral thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-188348.

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Conservation laws with uncertain initial and boundary conditions are approximated using a generalized polynomial chaos expansion approach where the solution is represented as a generalized Fourier series of stochastic basis functions, e.g. orthogonal polynomials or wavelets. The stochastic Galerkin method is used to project the governing partial differential equation onto the stochastic basis functions to obtain an extended deterministic system. The stochastic Galerkin and collocation methods are used to solve an advection-diffusion equation with uncertain viscosity. We investigate well-posedn

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Hunt, Stephen E. "Uncertainty Quantification Using Epi-Splines and Soft Information." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/7361.

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Approved for public release; distribution is unlimited<br>This thesis deals with the problem of measuring system performance in the presence of uncertainty. The system under consideration may be as simple as an Army vehicle subjected to a kinetic attack or as complex as the human cognitive process. Information about the system performance is found in the observed data points, which we call hard information, and may be collected from physical sensors, field test data, and computer simulations. Soft information is available from human sources such as subject-matter experts and analysts, and rep

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Lebon, Jérémy. "Towards multifidelity uncertainty quantification for multiobjective structural design." Phd thesis, Université de Technologie de Compiègne, 2013. http://tel.archives-ouvertes.fr/tel-01002392.

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This thesis aims at Multi-Objective Optimization under Uncertainty in structural design. We investigate Polynomial Chaos Expansion (PCE) surrogates which require extensive training sets. We then face two issues: high computational costs of an individual Finite Element simulation and its limited precision. From numerical point of view and in order to limit the computational expense of the PCE construction we particularly focus on sparse PCE schemes. We also develop a custom Latin Hypercube Sampling scheme taking into account the finite precision of the simulation. From the modeling point of vie

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Hristov, Peter O. "Numerical modelling and uncertainty quantification of biodiesel filters." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3024537/.

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This dissertation explores the design and analysis of computer models for filters used to separate water from biodiesel. Regulations concerning air pollution and increasing fossil fuel scarcity mandate the transition towards biofuels. Moreover, increasingly stringent standards for fuel cleanliness are introduced continually. Biodiesel exhibits strong affinity towards water, which makes its separation from the fuel challenging. Water in the fuel can cause problems, ranging from reduced performance to significant damage to the equipment. A model of the filter is needed to substitute costly or im

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Lal, Rajnesh. "Data assimilation and uncertainty quantification in cardiovascular biomechanics." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS088/document.

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Les simulations numériques des écoulements sanguins cardiovasculaires peuvent combler d’importantes lacunes dans les capacités actuelles de traitement clinique. En effet, elles offrent des moyens non invasifs pour quantifier l’hémodynamique dans le cœur et les principaux vaisseaux sanguins chez les patients atteints de maladies cardiovasculaires. Ainsi, elles permettent de recouvrer les caractéristiques des écoulements sanguins qui ne peuvent pas être obtenues directement à partir de l’imagerie médicale. Dans ce sens, des simulations personnalisées utilisant des informations propres aux patien

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Zhang, Zheng Ph D. Massachusetts Institute of Technology. "Uncertainty quantification for integrated circuits and microelectrornechanical systems." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99855.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 155-168).<br>Uncertainty quantification has become an important task and an emerging topic in many engineering fields. Uncertainties can be caused by many factors, including inaccurate component models, the stochastic nature of some design parameters, external environmental fluctuations (e.g., temperature variation), measurement noise, and so forth. In order to enable robust engineering des

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Chen, Qi. "Uncertainty quantification in assessment of damage ship survivability." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=19511.

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Ongoing developments in improving ship safety indicate the gradual transition from a compliance-based culture to a sustainable safety-oriented culture. Sophisticated methods, tools and techniques are demanded to address the dynamic behaviour of a ship in a physical environment. This is particularly true for investigating the flooding phenomenon of a damaged ship, a principal hazard endangering modern ships. In this respect, first-principles tools represent a rational and cost-effective approach to address it at both design and operational stages. Acknowledging the criticality of ship survivabi

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Abdollahzadeh, Asaad. "Adaptive algorithms for history matching and uncertainty quantification." Thesis, Heriot-Watt University, 2014. http://hdl.handle.net/10399/2752.

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Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification prob

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Pascual, Blanca. "Uncertainty quantification for complex structures : statics and dynamics." Thesis, Swansea University, 2012. https://cronfa.swan.ac.uk/Record/cronfa42987.

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Mulani, Sameer B. "Uncertainty Quantification in Dynamic Problems With Large Uncertainties." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/28617.

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This dissertation investigates uncertainty quantification in dynamic problems. The Advanced Mean Value (AMV) method is used to calculate probabilistic sound power and the sensitivity of elastically supported panels with small uncertainty (coefficient of variation). Sound power calculations are done using Finite Element Method (FEM) and Boundary Element Method (BEM). The sensitivities of the sound power are calculated through direct differentiation of the FEM/BEM/AMV equations. The results are compared with Monte Carlo simulation (MCS). An improved method is developed using AMV, metamodel, and

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Macatula, Romcholo Yulo. "Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99411.

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We consider uncertainty quantification using surrogate Gaussian processes. We take a previous sampling algorithm and provide a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of the resulting posterior distributions. We test methods on 1D deconvolution and 2D tomography. Our new methods improve on the previous algorithm, however fall short in some aspects to a typical Bayesian inference method.<br>Master of Science<br>Parameter uncertainty quantification seeks to determine b

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Huang, Jiangeng. "Sequential learning, large-scale calibration, and uncertainty quantification." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91935.

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With remarkable advances in computing power, computer experiments continue to expand the boundaries and drive down the cost of various scientific discoveries. New challenges keep arising from designing, analyzing, modeling, calibrating, optimizing, and predicting in computer experiments. This dissertation consists of six chapters, exploring statistical methodologies in sequential learning, model calibration, and uncertainty quantification for heteroskedastic computer experiments and large-scale computer experiments. For heteroskedastic computer experiments, an optimal lookahead based sequentia

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Vishwanathan, Aditya. "Uncertainty Quantification for Topology Optimisation of Aerospace Structures." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23922.

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The design and optimisation of aerospace structures is non-trivial. There are several reasons for this including, but not limited to, (1) complex problem instances (multiple objectives, constraints, loads, and boundary conditions), (2) the use of high fidelity meshes which impose significant computational burden, and (3) dealing with uncertainties in the engineering modelling. The last few decades have seen a considerable increase in research output dedicated to solving these problems, and yet the majority of papers neglect the effect of uncertainties and assume deterministic conditions. This

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Kraipeerapun, Pawalai. "Neural network classification based on quantification of uncertainty." Thesis, Kraipeerapun, Pawalai (2009) Neural network classification based on quantification of uncertainty. PhD thesis, Murdoch University, 2009. https://researchrepository.murdoch.edu.au/id/eprint/699/.

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This thesis deals with feedforward backpropagation neural networks and interval neutrosophic sets for the binary and multiclass classification problems. Neural networks are used to predict “true” and “false” output values. These results together with the uncertainty of type error and vagueness occurred in the prediction are then represented in the form of interval neutrosophic sets. Each element in an interval neutrosophic set consists of three membership values: truth, indeterminacy, and false. These three membership values are then used in the classification process. For binary classificatio

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Kraipeerapun, Pawalai. "Neural network classification based on quantification of uncertainty." Kraipeerapun, Pawalai (2009) Neural network classification based on quantification of uncertainty. PhD thesis, Murdoch University, 2009. http://researchrepository.murdoch.edu.au/699/.

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This thesis deals with feedforward backpropagation neural networks and interval neutrosophic sets for the binary and multiclass classification problems. Neural networks are used to predict “true” and “false” output values. These results together with the uncertainty of type error and vagueness occurred in the prediction are then represented in the form of interval neutrosophic sets. Each element in an interval neutrosophic set consists of three membership values: truth, indeterminacy, and false. These three membership values are then used in the classification process. For binary classificatio

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Pacreau, Grégoire. "Operator Learning for Recommender Systems and Uncertainty Quantification." Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAX008.

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Cette thèse a pour sujet l'estimation d'opérateurs et l'établissement de bornes non-asymptotique sur leur précision. En première partie, nous traiterons de l'estimation de la covariance en présence de valeurs manquantes ou contaminées. Nous montrons qu'un simple débiaisage est minimax optimal et bat en pratique les procédures plus complexes de l'état de l'art.Dans une deuxième partie, nous étudierons un bandit linéaire multi-tâche, dont la matrice des coefficients de la régression peut se décomposer en le produit de matrices plus petites: la première est une matrice de représentation commune q

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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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Hale, II Lawrence Edmond. "Aerodynamic Uncertainty Quantification and Estimation of Uncertainty Quantified Performance of Unmanned Aircraft Using Non-Deterministic Simulations." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/74427.

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This dissertation addresses model form uncertainty quantification, non-deterministic simulations, and sensitivity analysis of the results of these simulations, with a focus on application to analysis of unmanned aircraft systems. The model form uncertainty quantification utilizes equation error to estimate the error between an identified model and flight test results. The errors are then related to aircraft states, and prediction intervals are calculated. This method for model form uncertainty quantification results in uncertainty bounds that vary with the aircraft state, narrower where consis

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Gilbert, Michael Stephen. "A Small-Perturbation Automatic-Differentiation (SPAD) Method for Evaluating Uncertainty in Computational Electromagnetics." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354742230.

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Blumer, Joel David. "Cross-scale model validation with aleatory and epistemic uncertainty." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53571.

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Nearly every decision must be made with a degree of uncertainty regarding the outcome. Decision making based on modeling and simulation predictions needs to incorporate and aggregate uncertain evidence. To validate multiscale simulation models, it may be necessary to consider evidence collected at a length scale that is different from the one at which a model predicts. In addition, traditional methods of uncertainty analysis do not distinguish between two types of uncertainty: uncertainty due to inherently random inputs, and uncertainty due to lack of information about the inputs. This thesis

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Dostert, Paul Francis. "Uncertainty quantification using multiscale methods for porous media flows." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2532.

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Lonsdale, Jack Henry. "Predictive modelling and uncertainty quantification of UK forest growth." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/16202.

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Forestry in the UK is dominated by coniferous plantations. Sitka spruce (Picea sitchensis) and Scots pine (Pinus sylvestris) are the most prevalent species and are mostly grown in single age mono-culture stands. Forest strategy for Scotland, England, and Wales all include efforts to achieve further afforestation. The aim of this afforestation is to provide a multi-functional forest with a broad range of benefits. Due to the time scale involved in forestry, accurate forecasts of stand productivity (along with clearly defined uncertainties) are essential to forest managers. These can be provided

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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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Phillips, Edward G. "Fast solvers and uncertainty quantification for models of magnetohydrodynamics." Thesis, University of Maryland, College Park, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3644175.

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<p> The magnetohydrodynamics (MHD) model describes the flow of electrically conducting fluids in the presence of magnetic fields. A principal application of MHD is the modeling of plasma physics, ranging from plasma confinement for thermonuclear fusion to astrophysical plasma dynamics. MHD is also used to model the flow of liquid metals, for instance in magnetic pumps, liquid metal blankets in fusion reactor concepts, and aluminum electrolysis. The model consists of a non-self-adjoint, nonlinear system of partial differential equations (PDEs) that couple the Navier-Stokes equations for fluid f

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