Relevant bibliographies by topics / Uncertainty Quantification model

Academic literature on the topic 'Uncertainty Quantification model'

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Published: 31 August 2024

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Journal articles on the topic "Uncertainty Quantification model"

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Salehghaffari, S., and 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, no. 10 (January 8, 2013): 2165–81. http://dx.doi.org/10.1177/0954406212473390.

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Uncertainties in material models and their influence on structural behavior and reliability are important considerations in analysis and design of structures. In this article, a methodology based on the evidence theory is presented for uncertainty quantification of constitutive models. The proposed methodology is applied to Johnson–Cook plasticity model while considering various sources of uncertainty emanating from experimental stress–strain data as well as method of fitting the model constants and representation of the nondimensional temperature. All uncertain parameters are represented in interval form. Rules for agreement, conflict, and ignorance relationships in the data are discussed and subsequently used to construct a belief structure for each uncertain material parameter. The material model uncertainties are propagated through nonlinear crush simulation of an aluminium alloy 6061-T6 circular tube under axial impact load. Surrogate modeling and global optimization techniques are used for efficient calculation of the propagated belief structure of the tube response, whereas Yager’s aggregation rule of evidence is used for multi-model consideration. Evidence-based uncertainty in the structural response is measured and presented in terms of belief, plausibility, and plausibility-decision values.
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Vallam, P., X. S. Qin, and J. J. Yu. "Uncertainty Quantification of Hydrologic Model." APCBEE Procedia 10 (2014): 219–23. http://dx.doi.org/10.1016/j.apcbee.2014.10.042.

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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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Franck, Isabell M., and P. S. Koutsourelakis. "Constitutive model error and uncertainty quantification." PAMM 17, no. 1 (December 2017): 865–68. http://dx.doi.org/10.1002/pamm.201710400.

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de Vries, Douwe K., and Paul M. J. Den Van Hof. "Quantification of model uncertainty from data." International Journal of Robust and Nonlinear Control 4, no. 2 (1994): 301–19. http://dx.doi.org/10.1002/rnc.4590040206.

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Kamga, P. H. T., B. Li, M. McKerns, L. H. Nguyen, M. Ortiz, H. Owhadi, and T. J. Sullivan. "Optimal uncertainty quantification with model uncertainty and legacy data." Journal of the Mechanics and Physics of Solids 72 (December 2014): 1–19. http://dx.doi.org/10.1016/j.jmps.2014.07.007.

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Liu, Chang, and Duane A. McVay. "Continuous Reservoir-Simulation-Model Updating and Forecasting Improves Uncertainty Quantification." SPE Reservoir Evaluation & Engineering 13, no. 04 (August 12, 2010): 626–37. http://dx.doi.org/10.2118/119197-pa.

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Summary Most reservoir-simulation studies are conducted in a static context—at a single point in time using a fixed set of historical data for history matching. Time and budget constraints usually result in significant reduction in the number of uncertain parameters and incomplete exploration of the parameter space, which results in underestimation of forecast uncertainty and less-than-optimal decision making. Markov Chain Monte Carlo (MCMC) methods have been used in static studies for rigorous exploration of the parameter space for quantification of forecast uncertainty, but these methods suffer from long burn-in times and many required runs for chain stabilization. In this paper, we apply the MCMC in a real-time reservoirmodeling application. The system operates in a continuous process of data acquisition, model calibration, forecasting, and uncertainty quantification. The system was validated on the PUNQ (production forecasting with uncertainty quantification) synthetic reservoir in a simulated multiyear continuous-modeling scenario, and it yielded probabilistic forecasts that narrowed with time. Once the continuous MCMC simulation process has been established sufficiently, the continuous approach usually allows generation of a reasonable probabilistic forecast at a particular point in time with many fewer models than the traditional application of the MCMC method in a one-time, static simulation study starting at the same time. Operating continuously over the many years of typical reservoir life, many more realizations can be run than with traditional approaches. This allows more-thorough investigation of the parameter space and more-complete quantification of forecast uncertainty. More importantly, the approach provides a mechanism for, and can thus encourage, calibration of uncertainty estimates over time. Greater investigation of the uncertain parameter space and calibration of uncertainty estimates by using a continuous modeling process should improve the reliability of probabilistic forecasts significantly.
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Cheng, Xi, Clément Henry, Francesco P. Andriulli, Christian Person, and 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, no. 7 (April 9, 2020): 2586. http://dx.doi.org/10.3390/ijerph17072586.

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This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.
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Sun, Xianming, and Michèle Vanmaele. "Uncertainty Quantification of Derivative Instruments." East Asian Journal on Applied Mathematics 7, no. 2 (May 2017): 343–62. http://dx.doi.org/10.4208/eajam.100316.270117a.

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AbstractModel and parameter uncertainties are common whenever some parametric model is selected to value a derivative instrument. Combining the Monte Carlo method with the Smolyak interpolation algorithm, we propose an accurate efficient numerical procedure to quantify the uncertainty embedded in complex derivatives. Except for the value function being sufficiently smooth with respect to the model parameters, there are no requirements on the payoff or candidate models. Numerical tests carried out quantify the uncertainty of Bermudan put options and down-and-out put options under the Heston model, with each model parameter specified in an interval.
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Herty, Michael, and Elisa Iacomini. "Uncertainty quantification in hierarchical vehicular flow models." Kinetic and Related Models 15, no. 2 (2022): 239. http://dx.doi.org/10.3934/krm.2022006.

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<p style='text-indent:20px;'>We consider kinetic vehicular traffic flow models of BGK type [<xref ref-type="bibr" rid="b24">24</xref>]. Considering different spatial and temporal scales, those models allow to derive a hierarchy of traffic models including a hydrodynamic description. In this paper, the kinetic BGK–model is extended by introducing a parametric stochastic variable to describe possible uncertainty in traffic. The interplay of uncertainty with the given model hierarchy is studied in detail. Theoretical results on consistent formulations of the stochastic differential equations on the hydrodynamic level are given. The effect of the possibly negative diffusion in the stochastic hydrodynamic model is studied and numerical simulations of uncertain traffic situations are presented.</p>
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Dissertations / Theses on the topic "Uncertainty Quantification model"

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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 traditional computer model in the sense that repeated execution results in different outcomes from a stochastic simulation. This additional uncertainty in the simulation model requires to be handled accordingly in any calibration set up. Gaussian process (GP) emulator replaces the actual computer simulation when it is expensive to run and the budget is limited. However, traditional GP interpolator models the mean and/or variance of the simulation output as function of input. For a simulation where marginal gaussianity assumption is not appropriate, it does not suffice to emulate only the mean and/or variance. We present two different approaches addressing the non-gaussianity behavior of an emulator, by (1) incorporating quantile regression in GP for multivariate output, (2) approximating using finite mixture of gaussians. These emulators are also used to calibrate and make forward predictions in the context of an Agent Based disease model which models the Ebola epidemic outbreak in 2014 in West Africa. The third approach employs a sequential scheme which periodically updates the uncertainty inn the computer model input as data becomes available in an online fashion. Unlike other two methods which use an emulator in place of the actual simulation, the sequential approach relies on repeated run of the actual, potentially expensive simulation.
Doctor of Philosophy
Mathematical models are versatile and often provide accurate description of physical events. Scientific models are used to study such events in order to gain understanding of the true underlying system. These models are often complex in nature and requires advance algorithms to solve their governing equations. Outputs from these models depend on external information (also called model input) supplied by the user. Model inputs may or may not have a physical meaning, and can sometimes be only specific to the scientific model. More often than not, optimal values of these inputs are unknown and need to be estimated from few actual observations. This process is known as inverse problem, i.e. inferring the input from the output. The inverse problem becomes challenging when the mathematical model is stochastic in nature, i.e., multiple execution of the model result in different outcome. In this dissertation, three methodologies are proposed that talk about the calibration and prediction of a stochastic disease simulation model which simulates contagion of an infectious disease through human-human contact. The motivating examples are taken from the Ebola epidemic in West Africa in 2014 and seasonal flu in New York City in USA.
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White, Jeremy. "Computer Model Inversion and Uncertainty Quantification in the Geosciences." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5329.

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The subject of this dissertation is use of computer models as data analysis tools in several different geoscience settings, including integrated surface water/groundwater modeling, tephra fallout modeling, geophysical inversion, and hydrothermal groundwater modeling. The dissertation is organized into three chapters, which correspond to three individual publication manuscripts. In the first chapter, a linear framework is developed to identify and estimate the potential predictive consequences of using a simple computer model as a data analysis tool. The framework is applied to a complex integrated surface-water/groundwater numerical model with thousands of parameters. Several types of predictions are evaluated, including particle travel time and surface-water/groundwater exchange volume. The analysis suggests that model simplifications have the potential to corrupt many types of predictions. The implementation of the inversion, including how the objective function is formulated, what minimum of the objective function value is acceptable, and how expert knowledge is enforced on parameters, can greatly influence the manifestation of model simplification. Depending on the prediction, failure to specifically address each of these important issues during inversion is shown to degrade the reliability of some predictions. In some instances, inversion is shown to increase, rather than decrease, the uncertainty of a prediction, which defeats the purpose of using a model as a data analysis tool. In the second chapter, an efficient inversion and uncertainty quantification approach is applied to a computer model of volcanic tephra transport and deposition. The computer model simulates many physical processes related to tephra transport and fallout. The utility of the approach is demonstrated for two eruption events. In both cases, the importance of uncertainty quantification is highlighted by exposing the variability in the conditioning provided by the observations used for inversion. The worth of different types of tephra data to reduce parameter uncertainty is evaluated, as is the importance of different observation error models. The analyses reveal the importance using tephra granulometry data for inversion, which results in reduced uncertainty for most eruption parameters. In the third chapter, geophysical inversion is combined with hydrothermal modeling to evaluate the enthalpy of an undeveloped geothermal resource in a pull-apart basin located in southeastern Armenia. A high-dimensional gravity inversion is used to define the depth to the contact between the lower-density valley fill sediments and the higher-density surrounding host rock. The inverted basin depth distribution was used to define the hydrostratigraphy for the coupled groundwater-flow and heat-transport model that simulates the circulation of hydrothermal fluids in the system. Evaluation of several different geothermal system configurations indicates that the most likely system configuration is a low-enthalpy, liquid-dominated geothermal system.
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Park, Inseok. "Quantification of Multiple Types of Uncertainty in Physics-Based Simulation." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1348702461.

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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 examines and applies a Bayesian approach for model parameter validation that uses generalized interval probability to separate these two types of uncertainty. A generalized interval Bayes’ rule (GIBR) is used to combine the evidence and update belief in the validity of parameters. The sensitivity of completeness and soundness for interval range estimation in GIBR is investigated. Several approaches to represent complete ignorance of probabilities’ values are tested. The result from the GIBR method is verified using Monte Carlo simulations. The method is first applied to validate the parameter set for a molecular dynamics simulation of defect formation due to radiation. Evidence is supplied by the comparison with physical experiments. Because the simulation includes variables whose effects are not directly observable, an expanded form of GIBR is implemented to incorporate the uncertainty associated with measurement in belief update. In a second example, the proposed method is applied to combining the evidence from two models of crystal plasticity at different length scales.
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Ezvan, Olivier. "Multilevel model reduction for uncertainty quantification in computational structural dynamics." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1109/document.

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Ce travail de recherche présente une extension de la construction classique des modèles réduits (ROMs) obtenus par analyse modale, en dynamique numérique des structures linéaires. Cette extension est basée sur une stratégie de projection multi-niveau, pour l'analyse dynamique des structures complexes en présence d'incertitudes. De nos jours, il est admis qu'en dynamique des structures, la prévision sur une large bande de fréquence obtenue à l'aide d'un modèle éléments finis doit être améliorée en tenant compte des incertitudes de modèle induites par les erreurs de modélisation, dont le rôle croît avec la fréquence. Dans un tel contexte, l'approche probabiliste non-paramétrique des incertitudes est utilisée, laquelle requiert l'introduction d'un ROM. Par conséquent, ces deux aspects, évolution fréquentielle des niveaux d'incertitudes et réduction de modèle, nous conduisent à considérer le développement d'un ROM multi-niveau, pour lequel les niveaux d'incertitudes dans chaque partie de la bande de fréquence peuvent être adaptés. Dans cette thèse, on s'intéresse à l'analyse dynamique de structures complexes caractérisées par la présence de plusieurs niveaux structuraux, par exemple avec un squelette rigide qui supporte diverses sous-parties flexibles. Pour de telles structures, il est possible d'avoir, en plus des modes élastiques habituels dont les déplacements associés au squelette sont globaux, l'apparition de nombreux modes élastiques locaux, qui correspondent à des vibrations prédominantes des sous-parties flexibles. Pour ces structures complexes, la densité modale est susceptible d'augmenter fortement dès les basses fréquences (BF), conduisant, via la méthode d'analyse modale, à des ROMs de grande dimension (avec potentiellement des milliers de modes élastiques en BF). De plus, de tels ROMs peuvent manquer de robustesse vis-à-vis des incertitudes, en raison des nombreux déplacements locaux qui sont très sensibles aux incertitudes. Il convient de noter qu'au contraire des déplacements globaux de grande longueur d'onde caractérisant la bande BF, les déplacements locaux associés aux sous-parties flexibles de la structure, qui peuvent alors apparaître dès la bande BF, sont caractérisés par de courtes longueurs d'onde, similairement au comportement dans la bande hautes fréquences (HF). Par conséquent, pour les structures complexes considérées, les trois régimes vibratoires BF, MF et HF se recouvrent, et de nombreux modes élastiques locaux sont entremêlés avec les modes élastiques globaux habituels. Cela implique deux difficultés majeures, concernant la quantification des incertitudes d'une part et le coût numérique d'autre part. L'objectif de cette thèse est alors double. Premièrement, fournir un ROM stochastique multi-niveau qui est capable de rendre compte de la variabilité hétérogène introduite par le recouvrement des trois régimes vibratoires. Deuxièmement, fournir un ROM prédictif de dimension réduite par rapport à celui de l'analyse modale. Une méthode générale est présentée pour la construction d'un ROM multi-niveau, basée sur trois bases réduites (ROBs) dont les déplacements correspondent à l'un ou l'autre des régimes vibratoires BF, MF ou HF (associés à des déplacements de type BF, de type MF ou bien de type HF). Ces ROBs sont obtenues via une méthode de filtrage utilisant des fonctions de forme globales pour l'énergie cinétique (par opposition aux fonctions de forme locales des éléments finis). L'implémentation de l'approche probabiliste non-paramétrique dans le ROM multi-niveau permet d'obtenir un ROM stochastique multi-niveau avec lequel il est possible d'attribuer un niveau d'incertitude spécifique à chaque ROB. L'application présentée est relative à une automobile, pour laquelle le ROM stochastique multi-niveau est identifié par rapport à des mesures expérimentales. Le ROM proposé permet d'obtenir une dimension réduite ainsi qu'une prévision améliorée, en comparaison avec un ROM stochastique classique
This work deals with an extension of the classical construction of reduced-order models (ROMs) that are obtained through modal analysis in computational linear structural dynamics. It is based on a multilevel projection strategy and devoted to complex structures with uncertainties. Nowadays, it is well recognized that the predictions in structural dynamics over a broad frequency band by using a finite element model must be improved in taking into account the model uncertainties induced by the modeling errors, for which the role increases with the frequency. In such a framework, the nonparametric probabilistic approach of uncertainties is used, which requires the introduction of a ROM. Consequently, these two aspects, frequency-evolution of the uncertainties and reduced-order modeling, lead us to consider the development of a multilevel ROM in computational structural dynamics, which has the capability to adapt the level of uncertainties to each part of the frequency band. In this thesis, we are interested in the dynamical analysis of complex structures in a broad frequency band. By complex structure is intended a structure with complex geometry, constituted of heterogeneous materials and more specifically, characterized by the presence of several structural levels, for instance, a structure that is made up of a stiff main part embedding various flexible sub-parts. For such structures, it is possible having, in addition to the usual global-displacements elastic modes associated with the stiff skeleton, the apparition of numerous local elastic modes, which correspond to predominant vibrations of the flexible sub-parts. For such complex structures, the modal density may substantially increase as soon as low frequencies, leading to high-dimension ROMs with the modal analysis method (with potentially thousands of elastic modes in low frequencies). In addition, such ROMs may suffer from a lack of robustness with respect to uncertainty, because of the presence of the numerous local displacements, which are known to be very sensitive to uncertainties. It should be noted that in contrast to the usual long-wavelength global displacements of the low-frequency (LF) band, the local displacements associated with the structural sub-levels, which can then also appear in the LF band, are characterized by short wavelengths, similarly to high-frequency (HF) displacements. As a result, for the complex structures considered, there is an overlap of the three vibration regimes, LF, MF, and HF, and numerous local elastic modes are intertwined with the usual global elastic modes. This implies two major difficulties, pertaining to uncertainty quantification and to computational efficiency. The objective of this thesis is thus double. First, to provide a multilevel stochastic ROM that is able to take into account the heterogeneous variability introduced by the overlap of the three vibration regimes. Second, to provide a predictive ROM whose dimension is decreased with respect to the classical ROM of the modal analysis method. A general method is presented for the construction of a multilevel ROM, based on three orthogonal reduced-order bases (ROBs) whose displacements are either LF-, MF-, or HF-type displacements (associated with the overlapping LF, MF, and HF vibration regimes). The construction of these ROBs relies on a filtering strategy that is based on the introduction of global shape functions for the kinetic energy (in contrast to the local shape functions of the finite elements). Implementing the nonparametric probabilistic approach in the multilevel ROM allows each type of displacements to be affected by a particular level of uncertainties. The method is applied to a car, for which the multilevel stochastic ROM is identified with respect to experiments, solving a statistical inverse problem. The proposed ROM allows for obtaining a decreased dimension as well as an improved prediction with respect to a classical stochastic ROM
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Chiang, Shen. "Hydrological model comparison and refinement through uncertainty recognition and quantification." 京都大学 (Kyoto University), 2005. http://hdl.handle.net/2433/144539.

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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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Rashidi, Mehrabadi Niloofar. "Power Electronics Design Methodologies with Parametric and Model-Form Uncertainty Quantification." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82934.

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Modeling and simulation have become fully ingrained into the set of design and development tools that are broadly used in the field of power electronics. To state simply, they represent the fastest and safest way to study a circuit or system, thus aiding in the research, design, diagnosis, and debugging phases of power converter development. Advances in computing technologies have also enabled the ability to conduct reliability and production yield analyses to ensure that the system performance can meet given requirements despite the presence of inevitable manufacturing variability and variations in the operating conditions. However, the trustworthiness of all the model-based design techniques depends entirely on the accuracy of the simulation models used, which, thus far, has not yet been fully considered. Prior to this research, heuristic safety factors were used to compensate for deviation of real system performance from the predictions made using modeling and simulation. This approach resulted invariably in a more conservative design process. In this research, a modeling and design approach with parametric and model-form uncertainty quantification is formulated to bridge the modeling and simulation accuracy and reliance gaps that have hindered the full exploitation of model-based design techniques. Prior to this research, a few design approaches were developed to account for variability in the design process; these approaches have not shown the capability to be applicable to complex systems. This research, however, demonstrates that the implementation of the proposed modeling approach is able to handle complex power converters and systems. A systematic study for developing a simplified test bed for uncertainty quantification analysis is introduced accordingly. For illustrative purposes, the proposed modeling approach is applied to the switching model of a modular multilevel converter to improve the existing modeling practice and validate the model used in the design of this large-scale power converter. The proposed modeling and design methodology is also extended to design optimization, where a robust multi-objective design and optimization approach with parametric and model form uncertainty quantification is proposed. A sensitivity index is defined accordingly as a quantitative measure of system design robustness, with regards to manufacturing variability and modeling inaccuracies in the design of systems with multiple performance functions. The optimum design solution is realized by exploring the Pareto Front of the enhanced performance space, where the model-form error associated with each design is used to modify the estimated performance measures. The parametric sensitivity of each design point is also considered to discern between cases and help identify the most parametrically-robust of the Pareto-optimal design solutions. To demonstrate the benefits of incorporating uncertainty quantification analysis into the design optimization from a more practical standpoint, a Vienna-type rectifier is used as a case study to compare the theoretical analysis with a comprehensive experimental validation. This research shows that the model-form error and sensitivity of each design point can potentially change the performance space and the resultant Pareto Front. As a result, ignoring these main sources of uncertainty in the design will result in incorrect decision-making and the choice of a design that is not an optimum design solution in practice.
Ph. D.
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Xie, Yimeng. "Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/71687.

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This dissertation focuses on three research projects: 1) construction of simultaneous prediction intervals/bounds for at least k out of m future observations; 2) semi-parametric degradation model for accelerated destructive degradation test (ADDT) data; and 3) spatial variable selection and application to Lyme disease data in Virginia. Followed by the general introduction in Chapter 1, the rest of the dissertation consists of three main chapters. Chapter 2 presents the construction of two-sided simultaneous prediction intervals (SPIs) or one-sided simultaneous prediction bounds (SPBs) to contain at least k out of m future observations, based on complete or right censored data from (log)-location-scale family of distributions. SPI/SPB calculated by the proposed procedure has exact coverage probability for complete and Type II censored data. In Type I censoring case, it has asymptotically correct coverage probability and reasonably good results for small samples. The proposed procedures can be extended to multiply-censored data or randomly censored data. Chapter 3 focuses on the analysis of ADDT data. We use a general degradation path model with correlated covariance structure to describe ADDT data. Monotone B-splines are used to modeling the underlying degradation process. A likelihood based iterative procedure for parameter estimation is developed. The confidence intervals of parameters are calculated using the nonparametric bootstrap procedure. Both simulated data and real datasets are used to compare the semi-parametric model with the existing parametric models. Chapter 4 studies the Lyme disease emergence in Virginia. The objective is to find important environmental and demographical covariates that are associated with Lyme disease emergence. To address the high-dimentional integral problem in the loglikelihood function, we consider the penalized quasi loglikelihood and the approximated loglikelihood based on Laplace approximation. We impose the adaptive elastic net penalty to obtain sparse estimation of parameters and thus to achieve variable selection of important variables. The proposed methods are investigated in simulation studies. We also apply the proposed methods to Lyme disease data in Virginia. Finally, Chapter 5 contains general conclusions and discussions for future work.
Ph. D.
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Karlén, Johan. "Uncertainty Quantification of a Large 1-D Dynamic Aircraft System Simulation Model." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120189.

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A 1-D dynamic simulation model of a new cooling system for the upcoming Gripen E aircraft has been developed in the Modelica-based tool Dymola in order to examine the cooling performance. These types of low-dimensioned simulation models, which generally are described by ordinary differential equations or differential-algebraic equations, are often used to describe entire fluid systems. These equations are easier to solve than partial differential equations, which are used in 2-D and 3-D simulation models. Some approximations and assumptions of the physical system have to be made when developing this type of 1-D dynamic simulation model. The impact from these approximations and assumptions can be examined with an uncertainty analysis in order to increase the understanding of the simulation results. Most uncertainty analysis methods are not practically feasible when analyzing large 1-D dynamic simulation models with many uncertainties, implying the importance to simplify these methods in order to make them practically feasible. This study was aimed at finding a method that is easy to realize with low computational expense and engineering workload. The evaluated simulation model consists of several sub-models that are linked together. These sub-models run much faster when simulated as standalone models, compared to running the total simulation model as a whole. It has been found that this feature of the sub-models can be utilized in an interval-based uncertainty analysis where the uncertainty parameter settings that give the minimum and maximum simulation model response can be derived. The number of simulations needed of the total simulation model, in order to perform an uncertainty analysis, is thereby significantly reduced. The interval-based method has been found to be enough for most simulations since the control software in the simulation model controls the liquid cooling temperature to a specific reference value. The control system might be able to keep this reference value, even for the worst case uncertainty combinations, implying no need to further analyze these simulations with a more refined uncertainty propagation, such as a probabilistic propagation approach, where different uncertainty combinations are examined. While the interval-based uncertainty analysis method lacks probability information it can still increase the understanding of the simulation results. It is also computationally inexpensive and does not rely on an accurate and time-consuming characterization of the probability distribution of the uncertainties. Uncertainties from all sub-models in the evaluated simulation model have not been included in the uncertainty analysis made in this thesis. These neglected sub-model uncertainties can be included using the interval-based method, as a future work. Also, a method for combining the interval-based method with aleatory uncertainties is proposed in the end of this thesis and can be examined.
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Books on the topic "Uncertainty Quantification model"

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Mao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-77348-9.

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Mao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-04090-0.

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Barthorpe, Robert, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74793-4.

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Atamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou, and Tyler Schoenherr, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04552-8.

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Barthorpe, Robert, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-12075-7.

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Atamturktur, Sez, Tyler Schoenherr, Babak Moaveni, and Costas Papadimitriou, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5.

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Barthorpe, Robert, Roland Platz, Israel Lopez, Babak Moaveni, and Costas Papadimitriou, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54858-6.

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Mao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0.

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Atamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou, and Tyler Schoenherr, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0.

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Platz, Roland, Garrison Flynn, Kyle Neal, and Scott Ouellette, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-37003-8.

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Book chapters on the topic "Uncertainty Quantification model"

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Sun, Ne-Zheng, and Alexander Sun. "Model Uncertainty Quantification." In Model Calibration and Parameter Estimation, 407–58. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6_10.

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Nouy, Anthony. "Low-Rank Tensor Methods for Model Order Reduction." In Handbook of Uncertainty Quantification, 857–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_21.

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Chen, Peng, and Christoph Schwab. "Model Order Reduction Methods in Computational Uncertainty Quantification." In Handbook of Uncertainty Quantification, 937–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_70.

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Nouy, Anthony. "Low-Rank Tensor Methods for Model Order Reduction." In Handbook of Uncertainty Quantification, 1–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_21-1.

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Chen, Peng, and Christoph Schwab. "Model Order Reduction Methods in Computational Uncertainty Quantification." In Handbook of Uncertainty Quantification, 1–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_70-1.

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Suárez-Taboada, María, Jeroen A. S. Witteveen, Lech A. Grzelak, and Cornelis W. Oosterlee. "Uncertainty Quantification and Heston Model." In Progress in Industrial Mathematics at ECMI 2016, 153–59. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63082-3_22.

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Jiang, Zhen, Paul D. Arendt, Daniel W. Apley, and Wei Chen. "Multi-response Approach to Improving Identifiability in Model Calibration." In Handbook of Uncertainty Quantification, 69–127. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_65.

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Jiang, Zhen, Paul D. Arendt, Daniel W. Apley, and Wei Chen. "Multi-response Approach to Improving Identifiability in Model Calibration." In Handbook of Uncertainty Quantification, 1–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_65-1.

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Bijak, Jakub, and Jason Hilton. "Uncertainty Quantification, Model Calibration and Sensitivity." In Towards Bayesian Model-Based Demography, 71–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83039-7_5.

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AbstractBetter understanding of the behaviour of agent-based models, aimed at embedding them in the broader, model-based line of scientific enquiry, requires a comprehensive framework for analysing their results. Seeing models as tools for experimenting in silico, this chapter discusses the basic tenets and techniques of uncertainty quantification and experimental design, both of which can help shed light on the workings of complex systems embedded in computational models. In particular, we look at: relationships between model inputs and outputs, various types of experimental design, methods of analysis of simulation results, assessment of model uncertainty and sensitivity, which helps identify the parts of the model that matter in the experiments, as well as statistical tools for calibrating models to the available data. We focus on the role of emulators, or meta-models – high-level statistical models approximating the behaviour of the agent-based models under study – and in particular, on Gaussian processes (GPs). The theoretical discussion is illustrated by applications to the Routes and Rumours model of migrant route formation introduced before.
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Gattiker, James, Kary Myers, Brian J. Williams, Dave Higdon, Marcos Carzolio, and Andrew Hoegh. "Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA." In Handbook of Uncertainty Quantification, 1867–907. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_58.

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Conference papers on the topic "Uncertainty Quantification model"

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Andrews, Stephen A., and Brandon M. Wilson. "Variational Bayesian Calibration of a PTW Material Strength Model for OFHC Copper." In ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/vvuq2023-108829.

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Abstract The strength of materials at high strain rates is a challenging problem for model development and calibration. Such models can span a regime in strain rate from 1 × 10−3 s−1 to 1 × 1012 s−1 and a regime in temperature of 0K to up to the material’s melting temperature. The limits of these regimes can be difficult and expensive to access experimentally. There is interest in understanding how well calibrations made at moderate strain rates and temperature can perform when applied to more extreme regimes. Variational Bayesian techniques have been shown to be computationally inexpensive methods to both calibrate a model and understand the uncertainties in model parameters. This investigation will calibrate the parameters of a Peston-Tonks-Wallace (PTW) material strength model to low and moderate strain rate experiments from quasi-static, and Hopkinson bar experiments performed on fully annealed Oxygen Free High Conductivity (OFHC) copper. Bayesian methods will be used to quantify the correlated uncertainty in these parameters. These uncertainties will propagated forward to a simulation of a Richtmyer-Meshkov instability experiment which exercise a higher strain rate regime. The effects of the model uncertainties on the predictive ability of the simulation will be observed. This will demonstrate a strategy for Bayesian model calibration and uncertainty quantification for parametric models with applications to physics processes outside high strain rate plastic deformation.
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Eshraghi, Shaun, Michael Carolan, Benjamin Perlman, and Francisco González III. "Finite Element Model Validation of Cryogenic DOT-113 Tank Car Side Impact Tests." In ASME 2024 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/vvuq2024-132617.

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Abstract The U.S. Department of Transportation’s (USDOT’s) Federal Railroad Administration (FRA) has sponsored a series of four full-scale side impact tests on specification DOT-113 railroad tank cars. A DOT-113 is a specially designed tank car intended to transport cryogenic liquid commodities. For each side impact test, researchers at the USDOT’s Volpe National Transportation Systems Center (Volpe Center) created a pre-test finite element (FE) model to estimate the overall force-time response of the impactor, puncture/non-puncture outcomes of the impacted tank car, global motions of the tank car, internal pressures within the tank car, and the energy absorbed by the tank car during the impact. While researchers have previously compared FE model results to test measurements for tank car side impact tests, there are currently no formal guidelines on what measurable level of agreement is an acceptable demonstration of FE model validation. This paper presents FE model validation of DOT-113 and DOT-113 surrogate side impact tests using a publicly available software named Correlation and Analysis Plus (CORA) [1] which was originally developed for automotive crashworthiness using models of anthropomorphic test devices, i.e., crash test dummies. The authors have previously presented FE model validation frameworks for impact simulations [2] and demonstrated FE model validation for non-cryogenic tank car side impacts [3] using CORA and another software called Roadside Verification and Validation Program (RSVVP) [4]. The authors have decided to use CORA in this paper because its validation metrics and rating procedures are included in an ISO technical specification for road vehicles (ISO/TS 18571:2014) [5]. Conversely, RSVVP is not incorporated in a US or international specification. The results indicate that CORA can be directly applied to tank car side impact model results using the procedures in ISO/TS 18571:2014 when the model does not self-terminate due to puncture. The FE models achieved excellent and good CORA scores for cases without puncture of the tank car. However, early termination of the FE model due to puncture disrupted the automated post-processing of the model results for the two tests that produced a puncture outcome. Further consideration is necessary to develop guidelines that can produce useful validation scores for FE models that include a puncture outcome.
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Kirsch, Jared, William Rider, and Nima Fathi. "Credibility Assessment of Machine Learning-Based Surrogate Model Predictions on NACA 0012 Airfoil Flow." In ASME 2024 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/vvuq2024-132964.

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Abstract The use of surrogate models in computational mechanics is an area of high interest due to the potential for significant savings in computational cost. However, assessment and presentation of evidence for surrogate model credibility has yet to reach a standard form. The present study utilizes a deep neural network as a surrogate for a computational fluid dynamics simulation in order to predict the coefficients of lift and drag on a NACA 0012 airfoil for various Reynolds numbers and angles of attack. Using best practices, the credibility of the underlying simulation predictions and of the surrogate model predictions are analyzed. Conclusions are drawn which should better inform future uses of surrogate models in the context of their credibility.
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Tartaruga, Irene, Jonathan E. Cooper, Georgia Georgiou, and Hamed Khodaparast. "Flutter Uncertainty Quantification for the S4T Model." In 55th AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1653.

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Aarts, Arne, Wil Michiels, and Peter Roelse. "Leveraging Partial Model Extractions using Uncertainty Quantification." In 2021 IEEE 10th International Conference on Cloud Networking (CloudNet). IEEE, 2021. http://dx.doi.org/10.1109/cloudnet53349.2021.9657130.

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Davis, Brad, Gregory Langone, and 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.

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Abstract Periodic updates to small caliber weapon systems and projectiles used in military and law enforcement have resulted in consistently increasing material penetration capabilities. With each new generation, ballistics technology outpaces the lifecycle replacement of live-fire training facilities. For this reason, it is necessary to develop and maintain constitutive material models for use in analyzing the effects new threats will have on existing facilities and for designing new training facilities using numerical methods. This project utilizes material testing data to characterize cellular concretes used in the construction of live-fire training facilities with a 13-parameter Holmquist-Johnson-Cook (HJC) concrete constitutive model. Various statistical tools are used in this analysis to successfully describe the importance of each model parameter and quantify their uncertainty. First, Bayesian linear regression was used to calibrate the parameters in the strength and pressure components of the HJC material model given testing data of cellular concrete. These uncertain parameters were then used to construct computer simulations of penetration and perforation experiments that were previously conducted by Collard and Lanham. Then, Latin Hypercube Sampling of the parameter space was used to generate training data for a Gaussian Process surrogate model of the computer simulation. Using the surrogate model, a global variance-based sensitivity analysis of the material model was completed by computing main and total effect Sobol indices. Finally, a Bayesian calibration of the computer simulation based on the physical experiments was conducted to fully characterize the stochastic behavior of the material subjected to perforation impacts. These approaches can be used to inform decision makers about the potential risk associated with existing facilities and by designers of future live fire training facilities.
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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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Giagopoulos, Dimitrios, Alexandros Arailopoulos, Ilias Zacharakis, and Eleni Pipili. "FINITE ELEMENT MODEL DEVELOPED AND MODAL ANALYSIS OF LARGE SCALE STEAM TURBINE ROTOR: QUANTIFICATION OF UNCERTAINTIES AND MODEL UPDATING." In 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2017. http://dx.doi.org/10.7712/120217.5349.16898.

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Laboulfie, Clément, Matthieu Balesdent, Loïc Brevault, Sébastien Da Veiga, François-Xavier Irisarri, Rodolphe Le Riche, and Jean-François Maire. "CALIBRATION OF MATERIAL MODEL PARAMETERS USING MIXED-EFFECTS MODEL." In 4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Research and Development for Computational Methods in Engineering Sciences (ICMES), 2021. http://dx.doi.org/10.7712/120221.8037.18933.

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El Garroussi, Siham, Matthias De Lozzo, Sophie Ricci, Didier Lucor, Nicole Goutal, Cédric Goeury, and Sébastien Boyaval. "UNCERTAINTY QUANTIFICATION IN A TWO-DIMENSIONAL RIVER HYDRAULIC MODEL." In 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2019. http://dx.doi.org/10.7712/120219.6339.18380.

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Reports on the topic "Uncertainty Quantification model"

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Gonzales, Lindsey M., Thomas M. Hall, Kendra L. Van Buren, Steven R. Anton, and Francois M. Hemez. Quantification of Prediction Bounds Caused by Model Form Uncertainty. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1095195.

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Lawrence, Earl Christopher, and Brian Phillip Weaver. Model Emulation and Calibration: Uncertainty Quantification and Making Inference with Simulation. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1514917.

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Weirs, V. Gregory. Dakota uncertainty quantification methods applied to the NEK-5000 SAHEX model. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1155019.

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Logan, R., C. Nitta, and S. Chidester. Estimating Parametric, Model Form, and Solution Contributions Using Integral Validation Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), February 2006. http://dx.doi.org/10.2172/894762.

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Hund, Lauren, and Justin Brown. Statistically Rigorous Uncertainty Quantification for Physical Parameter Model Calibration with Functional Output. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562417.

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Tezaur, Irina Kalashnikova, Maciej Balajewicz, Matthew F. Barone, Kevin Thomas Carlberg, Jeffrey A. Fike, and Erin E. Mussoni. Model Reduction for Compressible Cavity Simulations Towards Uncertainty Quantification of Structural Loading. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562432.

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Maulik, Romit, Virendra Ghate, William Pringle, Yan Feng, Vishwas Rao, Julie Bessac, and Bethany Lusch. Surrogate multi-fidelity data and model fusion forscientific discovery and uncertainty quantification inEarth System Models. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769781.

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Acquesta, Erin, Teresa Portone, Raj Dandekar, Chris Rackauckas, Rileigh Bandy, Jose Huerta, and India Dytzel. Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1888443.

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Wang, Dali, Shih-Chieh Kao, and Daniel Ricciuto. Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769696.

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Chung, Bub Dong, Young Lee Lee, Chan Eok Park, and Sang Yong Lee. Improvements to the RELAP5/MOD3 reflood model and uncertainty quantification of reflood peak clad temperature. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/393372.

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