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Teses / dissertações sobre o tema "Predictive uncertainty quantification"

Autor: Grafiati

Publicado: 5 de outubro de 2024

Última modificação: 31 de julho de 2025

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1

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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2

Gligorijevic, Djordje. "Predictive Uncertainty Quantification and Explainable Machine Learning in Healthcare." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/520057.

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Computer and Information Science<br>Ph.D.<br>Predictive modeling is an ever-increasingly important part of decision making. The advances in Machine Learning predictive modeling have spread across many domains bringing significant improvements in performance and providing unique opportunities for novel discoveries. A notably important domains of the human world are medical and healthcare domains, which take care of peoples' wellbeing. And while being one of the most developed areas of science with active research, there are many ways they can be improved. In particular, novel tools developed ba

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3

Zaffran, Margaux. "Post-hoc predictive uncertainty quantification : methods with applications to electricity price forecasting." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX033.

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L'essor d'algorithmes d'apprentissage statistique offre des perspectives prometteuses pour prévoir les prix de l'électricité. Cependant, ces méthodes fournissent des prévisions ponctuelles, sans indication du degré de confiance à leur accorder. Pour garantir un déploiement sûr de ces modèles prédictifs, il est crucial de quantifier leur incertitude prédictive. Cette thèse porte sur le développement d'intervalles prédictifs pour tout algorithme de prédiction. Bien que motivées par le secteur électrique, les méthodes développées, basées sur la prédiction conforme par partition (SCP), sont généri

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4

Riley, Matthew E. "Quantification of Model-Form, Predictive, and Parametric Uncertainties in Simulation-Based Design." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1314895435.

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5

Freeman, Jacob Andrew. "Optimization Under Uncertainty and Total Predictive Uncertainty for a Tractor-Trailer Base-Drag Reduction Device." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77168.

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One key outcome of this research is the design for a 3-D tractor-trailer base-drag reduction device that predicts a 41% reduction in wind-averaged drag coefficient at 57 mph (92 km/h) and that is relatively insensitive to uncertain wind speed and direction and uncertain deflection angles due to mounting accuracy and static aeroelastic loading; the best commercial device of non-optimized design achieves a 12% reduction at 65 mph. Another important outcome is the process by which the optimized design is obtained. That process includes verification and validation of the flow solver, a less comple

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6

Wu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.

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Reynolds-Averaged Navier-Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, a

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7

Cortesi, Andrea Francesco. "Predictive numerical simulations for rebuilding freestream conditions in atmospheric entry flows." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0021/document.

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Une prédiction fidèle des écoulements hypersoniques à haute enthalpie est capitale pour les missions d'entrée atmosphérique. Cependant, la présence d'incertitudes est inévitable, sur les conditions de l'écoulement libre comme sur d'autres paramètres des modèles physico-chimiques. Pour cette raison, une quantification rigoureuse de l'effet de ces incertitudes est obligatoire pour évaluer la robustesse et la prédictivité des simulations numériques. De plus, une reconstruction correcte des paramètres incertains à partir des mesures en vol peut aider à réduire le niveau d'incertitude sur les sorti

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8

Erbas, Demet. "Sampling strategies for uncertainty quantification in oil recovery prediction." Thesis, Heriot-Watt University, 2007. http://hdl.handle.net/10399/70.

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9

Whiting, Nolan Wagner. "Assessment of Model Validation, Calibration, and Prediction Approaches in the Presence of Uncertainty." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91903.

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Model validation is the process of determining the degree to which a model is an accurate representation of the true value in the real world. The results of a model validation study can be used to either quantify the model form uncertainty or to improve/calibrate the model. However, the model validation process can become complicated if there is uncertainty in the simulation and/or experimental outcomes. These uncertainties can be in the form of aleatory uncertainties due to randomness or epistemic uncertainties due to lack of knowledge. Four different approaches are used for addressing model

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10

Phadnis, Akash. "Uncertainty quantification and prediction for non-autonomous linear and nonlinear systems." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85476.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 189-197).<br>The science of uncertainty quantification has gained a lot of attention over recent years. This is because models of real processes always contain some elements of uncertainty, and also because real systems can be better described using stochastic components. Stochastic models can therefore be utilized to provide a most informative prediction of possible future states of the system. In light of the m

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11

Kim, Jee Yun. "Data-driven Methods in Mechanical Model Calibration and Prediction for Mesostructured Materials." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85210.

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Mesoscale design involving control of material distribution pattern can create a statistically heterogeneous material system, which has shown increased adaptability to complex mechanical environments involving highly non-uniform stress fields. Advances in multi-material additive manufacturing can aid in this mesoscale design, providing voxel level control of material property. This vast freedom in design space also unlocks possibilities within optimization of the material distribution pattern. The optimization problem can be divided into a forward problem focusing on accurate predication and a

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12

Zhang, Y. "Quantification of prediction uncertainty for principal components regression and partial least squares regression." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1433990/.

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Principal components regression (PCR) and partial least squares regression (PLS) are widely used in multivariate calibration in the fields of chemometrics, econometrics, social science and so forth, serving as alternative solutions to the problems which arise in ordinary least squares regression when explanatory variables are either collinear, or there are hundreds of explanatory variables with a relatively small sample size. Both PCR and PLS tackle the problems by constructing lower dimensional factors based on the explanatory variables. The extra step of factor construction makes the standar

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13

Zavar, Moosavi Azam Sadat. "Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82491.

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Simulations and modeling of large-scale systems are vital to understanding real world phenomena. However, even advanced numerical models can only approximate the true physics. The discrepancy between model results and nature can be attributed to different sources of uncertainty including the parameters of the model, input data, or some missing physics that is not included in the model due to a lack of knowledge or high computational costs. Uncertainty reduction approaches seek to improve the model accuracy by decreasing the overall uncertainties in models. Aiming to contribute to this area,

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14

Kacker, Shubhra. "The Role of Constitutive Model in Traumatic Brain Injury Prediction." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563874757653453.

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15

Mohamed, Lina Mahgoub Yahya. "Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction." Thesis, Heriot-Watt University, 2011. http://hdl.handle.net/10399/2435.

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Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this

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16

Smit, Jacobus Petrus Johannes. "The quantification of prediction uncertainty associated with water quality models using Monte Carlo Simulation." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85814.

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Thesis (MEng)--Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: Water Quality Models are mathematical representations of ecological systems and they play a major role in the planning and management of water resources and aquatic environments. Important decisions concerning capital investment and environmental consequences often rely on the results of Water Quality Models and it is therefore very important that decision makers are aware and understand the uncertainty associated with these models. The focus of this study was on the use of Monte Carlo Simulation for the quantification of predi

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17

Tamssaouet, Ferhat. "Towards system-level prognostics : modeling, uncertainty propagation and system remaining useful life prediction." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0079.

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Le pronostic est le processus de prédiction de la durée de vie résiduelle utile (RUL) des composants, sous-systèmes ou systèmes. Cependant, jusqu'à présent, le pronostic a souvent été abordé au niveau composant sans tenir compte des interactions entre les composants et l'impact de l'environnement, ce qui peut conduire à une mauvaise prédiction du temps de défaillance dans des systèmes complexes. Dans ce travail, une approche de pronostic au niveau du système est proposée. Cette approche est basée sur un nouveau cadre de modélisation : le modèle d'inopérabilité entrée-sortie (IIM), qui permet d

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18

Puckett, Kerri A. "Uncertainty quantification in predicting deep aquifer recharge rates, with applicability in the Powder River Basin, Wyoming." Laramie, Wyo. : University of Wyoming, 2008. http://proquest.umi.com/pqdweb?did=1594477301&sid=2&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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19

Sun, Yuming. "Closing the building energy performance gap by improving our predictions." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52285.

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Increasing studies imply that predicted energy performance of buildings significantly deviates from actual measured energy use. This so-called "performance gap" may undermine one's confidence in energy-efficient buildings, and thereby the role of building energy efficiency in the national carbon reduction plan. Closing the performance gap becomes a daunting challenge for the involved professions, stimulating them to reflect on how to investigate and better understand the size, origins, and extent of the gap. The energy performance gap underlines the lack of prediction capability of current bui

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20

Messerly, Richard Alma. "How a Systematic Approach to Uncertainty Quantification Renders Molecular Simulation a Quantitative Tool in Predicting the Critical Constants for Large n-Alkanes." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6598.

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Accurate thermophysical property data are crucial for designing efficient chemical processes. For this reason, the Design Institute for Physical Properties (DIPPR 801) provides evaluated experimental data and prediction of various thermophysical properties. The critical temperature (Tc), critical density (ρc), critical pressure (Pc), critical compressibility factor (Zc), and normal boiling point (Tb) are important constants to check for thermodynamic consistency and to estimate other properties. The n-alkane family is of primary interest because it is generally assumed that other families of c

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21

Bulthuis, Kevin. "Towards robust prediction of the dynamics of the Antarctic ice sheet: Uncertainty quantification of sea-level rise projections and grounding-line retreat with essential ice-sheet models." Doctoral thesis, Universite Libre de Bruxelles, 2020. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/301049.

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Recent progress in the modelling of the dynamics of the Antarctic ice sheet has led to a paradigm shift in the perception of the Antarctic ice sheet in a changing climate. New understanding of the dynamics of the Antarctic ice sheet now suggests that the response of the Antarctic ice sheet to climate change will be driven by instability mechanisms in marine sectors. As concerns have grown about the response of the Antarctic ice sheet in a warming climate, interest has grown simultaneously in predicting with quantified uncertainty the evolution of the Antarctic ice sheet and in clarifying the r

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22

Calanni, Fraccone Giorgio M. "Bayesian networks for uncertainty estimation in the response of dynamic structures." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24714.

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Thesis (Ph.D.)--Aerospace Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Dr. Vitali Volovoi; Committee Co-Chair: Dr. Massimo Ruzzene; Committee Member: Dr. Andrew Makeev; Committee Member: Dr. Dewey Hodges; Committee Member: Dr. Peter Cento

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23

Rafael-Palou, Xavier. "Detection, quantification, malignancy prediction and growth forecasting of pulmonary nodules using deep learning in follow-up CT scans." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/672964.

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Nowadays, lung cancer assessment is a complex and tedious task mainly per- formed by radiological visual inspection of suspicious pulmonary nodules, using computed tomography (CT) scan images taken to patients over time. Several computational tools relying on conventional artificial intelligence and computer vision algorithms have been proposed for supporting lung cancer de- tection and classification. These solutions mostly rely on the analysis of indi- vidual lung CT images of patients and on the use of hand-crafted image de- scriptors. Unfortunately, this makes them unable to cope

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24

GRIFFINI, DUCCIO. "Development of Predictive Models for Synchronous Thermal Instability." Doctoral thesis, 2017. http://hdl.handle.net/2158/1081044.

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The increasing demand of higher efficiency and increased equipment compactness is pushing the modern rotordynamic design towards higher and higher bearing peripheral speed. Due to the increased viscous dissipation, modern fluid film bearings are prone to the development of complex thermal phenomena that, under certain conditions, can result in synchronous thermal instability, often referred to as Morton effect. Although the phenomenon is known and studied from the late 1970s a lack of knowledge is highlighted in literature and the strategy to approach its prediction and analysis is yet debated

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25

Hawkins-Daarud, Andrea Jeanine. "Toward a predictive model of tumor growth." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-05-3395.

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In this work, an attempt is made to lay out a framework in which models of tumor growth can be built, calibrated, validated, and differentiated in their level of goodness in such a manner that all the uncertainties associated with each step of the modeling process can be accounted for in the final model prediction. The study can be divided into four basic parts. The first involves the development of a general family of mathematical models of interacting species representing the various constituents of living tissue, which generalizes those previously available in the literature. In this theor

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26

Romero, Cuellar Jonathan. "Improving hydrological post-processing for assessing the conditional predictive uncertainty of monthly streamflows." Doctoral thesis, 2020. http://hdl.handle.net/10251/133999.

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[ES] La cuantificación de la incertidumbre predictiva es de vital importancia para producir predicciones hidrológicas confiables que soporten y apoyen la toma de decisiones en el marco de la gestión de los recursos hídricos. Los post-procesadores hidrológicos son herramientas adecuadas para estimar la incertidumbre predictiva de las predicciones hidrológicas (salidas del modelo hidrológico). El objetivo general de esta tesis es mejorar los métodos de post-procesamiento hidrológico para estimar la incertidumbre predictiva de caudales mensuales. Esta tesis pretende resolver dos problemas del pos

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27

DI, ROCCO FEDERICO. "Predictive modeling analysis of a wet cooling tower - Adjoint sensitivity analysis, uncertainty quantification, data assimilation, model calibration, best-estimate predictions with reduced uncertainties." Doctoral thesis, 2018. http://hdl.handle.net/11573/1091474.

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It is common practice, in the modern era, to base the process of understanding and eventually predicting the behavior of complex physical systems upon simulating operational situations through system codes. In order to provide a more thorough and accurate comprehension of the system dynamics, these numerical simulations are often and preferably flanked by experimental measurements. In practice, repeated measurements of the same physical quantity produce values differing from each other and from the measured quantity true value, which remains unknown; the errors leading to this variation in res

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28

Suryawanshi, Anup Arvind. "Uncertainty Quantification in Flow and Flow Induced Structural Response." Thesis, 2015. http://etd.iisc.ac.in/handle/2005/3875.

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Response of flexible structures — such as cable-supported bridges and aircraft wings — is associated with a number of uncertainties in structural and flow parameters. This thesis is aimed at efficient uncertainty quantification in a few such flow and flow-induced structural response problems. First, the uncertainty quantification in the lift force exerted on a submerged body in a potential flow is considered. To this end, a new method — termed here as semi-intrusive stochastic perturbation (SISP) — is proposed. A sensitivity analysis is also performed, where for the global sensitivity analysi

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29

Suryawanshi, Anup Arvind. "Uncertainty Quantification in Flow and Flow Induced Structural Response." Thesis, 2015. http://etd.iisc.ernet.in/2005/3875.

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Response of flexible structures — such as cable-supported bridges and aircraft wings — is associated with a number of uncertainties in structural and flow parameters. This thesis is aimed at efficient uncertainty quantification in a few such flow and flow-induced structural response problems. First, the uncertainty quantification in the lift force exerted on a submerged body in a potential flow is considered. To this end, a new method — termed here as semi-intrusive stochastic perturbation (SISP) — is proposed. A sensitivity analysis is also performed, where for the global sensitivity analysi

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30

Sawlan, Zaid A. "Statistical Analysis and Bayesian Methods for Fatigue Life Prediction and Inverse Problems in Linear Time Dependent PDEs with Uncertainties." Diss., 2018. http://hdl.handle.net/10754/629731.

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This work employs statistical and Bayesian techniques to analyze mathematical forward models with several sources of uncertainty. The forward models usually arise from phenomenological and physical phenomena and are expressed through regression-based models or partial differential equations (PDEs) associated with uncertain parameters and input data. One of the critical challenges in real-world applications is to quantify uncertainties of the unknown parameters using observations. To this purpose, methods based on the likelihood function, and Bayesian techniques constitute the two main statisti

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31

Rasheed, Md Muhibur. "Predicting multibody assembly of proteins." Thesis, 2014. http://hdl.handle.net/2152/26149.

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This thesis addresses the multi-body assembly (MBA) problem in the context of protein assemblies. [...] In this thesis, we chose the protein assembly domain because accurate and reliable computational modeling, simulation and prediction of such assemblies would clearly accelerate discoveries in understanding of the complexities of metabolic pathways, identifying the molecular basis for normal health and diseases, and in the designing of new drugs and other therapeutics. [...] [We developed] F²Dock (Fast Fourier Docking) which includes a multi-term function which includes both a statistical the

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32

Xu, Chicheng. "Reservoir description with well-log-based and core-calibrated petrophysical rock classification." 2013. http://hdl.handle.net/2152/21315.

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Rock type is a key concept in modern reservoir characterization that straddles multiple scales and bridges multiple disciplines. Reservoir rock classification (or simply rock typing) has been recognized as one of the most effective description tools to facilitate large-scale reservoir modeling and simulation. This dissertation aims to integrate core data and well logs to enhance reservoir description by classifying reservoir rocks in a geologically and petrophysically consistent manner. The main objective is to develop scientific approaches for utilizing multi-physics rock data at different ti

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