Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 18 of 18 for “"Input Uncertainty"”.
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Classification under input uncertainty with support vector machines
Uncertainty can exist in any measurement of data describing the real world. Many machine learning approaches attempt to model any uncertainty in the form of additive noise on the target, which can be effective for simple models. However, for more complex models, and where a richer description of …
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Neural Network Gaussian Process considering Input Uncertainty and Application to Composite Structures Assembly
… of composite materials; and (ii) inevitable uncertainty in the assembly process. To overcome those problems, we propose a neural network Gaussian process model considering input uncertainty for composite structures assembly. Deep architecture of our model allows us to approximate a complex …
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Quantifying and reducing Input modelling error in simulation
… in the field of quantifying and reducing input modelling error in computer simulation. Input modelling error is the uncertainty in the output of a simulation that propagates from the errors in the input models used to drive it. When the input models are estimated from observations of the …
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A hierarchical Bayesian calibration framework for quantifying input uncertainties in thermal-hydraulics simulation models
In the framework of Best Estimate plus Uncertainty (BEPU) methodology, the uncertainties involved in simulations must be quantified to prove that the investigated design is reasonable and acceptable. The predictive uncertainties are usually calculated by propagating input uncertainties through the …
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Parameter estimation for unsaturated flow models
… precisely. A numerical study on the effects of input uncertainty on estimation results indicates that complexity of the model that can be meaningfully identified from a given set of data is controlled by the level of data uncertainty. When data error from several sources was compounded, broad …
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ON SOLVING MULTI-OBJECTIVE SIMULATION OPTIMIZATION BY OPTIMAL COMPUTING BUDGET ALLOCATION AND RANDOM SEARCH
… to attack robust Ranking and Selection with input uncertainty.
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Assessing uncertainty propagation of precipitation input in hydrometeorological ensemble forecasting systems
… of the thesis is the assessment of precipitation input uncertainty into hydrological response in hydrometeorological ensemble systems for flood prediction. The study has been preliminary focused on the development of a hydrometeorological system that couples a statistical precipitation downscaling …
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Wind-Driven Sea Surface Wave and Coastal Water Level Dynamics Across Different Spatial Scales
… large domains and the error induced by model input uncertainty. Increased mean sea elevation is found to increase nearshore wave heights and heighten risk of infrastructure overtopping and changes to the intertidal zone. Detailed observations of surface wave runup on unsaturated beach face …
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Simulation-Based Decision Making with Streaming Data
… simulation. At the same time, the stochastic inputs driving these systems are often unknown and must be inferred from data, which may be collected gradually over time. These challenges motivate the development of data-driven methodologies that integrate simulation, optimization, and learning …
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Essays on Healthcare Coordination
… we propose and test the hypothesis that input uncertainty, knowledge insufficiency, and prevalence influence hospitals’ decision to participate in ACOs, as well as its effect on cost and quality performance. The theoretical contribution of this dissertation focuses on clarifying how these …
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A fully Bayesian approach to uncertainty quantification of groundwater models
… groundwater models are often subject to input data errors, as some of the input forcings (such as recharge and well pumping rates) are unknown or estimated. Furthermore, model structural error is ubiquitous, due to simplification and/or misrepresentation of the real system. The presence …
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Optimal control and model reduction for wave energy systems: A moment-based approach
… solutions with respect to both system, and input uncertainty, providing an efficient method to compute the energy-maximising control law for WECs, under different modelling assumptions. Throughout this thesis both model reduction, and optimal control frameworks, are presented for a general …
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Uncertainty and sensitivity analysis of computational simulations of industrial jet flows
… with a stochastic approach. For this purpose, uncertainty quantification and global sensitivity analysis have been carried out in two industrial jet flows with different objectives. First, an impinging swirling air jet for heat transfer purposes is generated by an axisymmetric rotating pipe. …
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Probabilistic Musculoskeletal Simulation Methods to Address Intersegmental Dependencies of the Knee, Hip, and Spine
… hip, and spine regional interdependence by using input distributions to quantify the impact of variability on the range of possible output variables. Outputs from probabilistic methods include variable interaction effects and provides sensitivity information, resulting in a more comprehensive …
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Biosorption of uranium and its effect on uranium transport in groundewater
… sorption. This was done by first evaluating the uncertainty associated with uranium equilibrium speciation and its effect on the prediction of uranium sorption to minerals. Then, the partition coefficient between U(VI) and the microbial species Geobacter uraniireducens and Acholeplasma palmae …
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Methodology for technology evaluation under uncertainty and its application in advanced coal gasification processes
… state of knowledge and evaluate the impact of uncertainty in every phase of the R&D process. A rigorous investigation of the effect of uncertainty on IGCC system requires accurate quantification of input uncertainty and efficient propagation of uncertainty through system models. This thesis …
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Dealing With Uncertainty in Engineering and Management Practices
… presented to assess traffic noise impact under uncertainty (Peng and Mayorga, 2008). Three uncertain inputs, namely, traffic flow, traffic speed and traffic components, are represented by probability distributions. Monte Carlo simulation is performed to generate these noise distributions. …
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Neural parameter inference for large-scale multi-agent systems
… throughout this work is the quantification of uncertainty in neural network predictions, approached in a tractable and computationally efficient manner. We explore various strategies, including propagating input uncertainty through the network, ensemble training, and a novel method inspired by …