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 20 of 25 for “"Gaussian Process Regression (gpr)"”.
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DATA-DRIVEN MODELING AND CONTROL FOR TIME-VARYING MULTISTAGE MANUFACTURING PROCESSES
This thesis aims to develop a unified data-driven process modeling and control framework for quality improvement of nonlinear and time-varying Multistage Manufacturing Processes (MMPs). We first investigate the impact of modeling accuracy on the residual controls which are acknowledged as the main …
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Machine learning approach to model the microstructure and strength of nickel superalloys
… predict their microstructure and strength. The Gaussian process regression (GPR) models of microstructure are shown to be just as good at interpolation as traditional CALPHAD models, with advantages in speed, retrainability, incorporation of non-equilibrium effects, and the effective inclusion …
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Gaussian Process Regression for a Single Underlying Autocallable Security
… the use of the machine learning technique Gaussian Process Regression (GPR) as a faster pricing alternative to Monte Carlo simula- tions. The speed of calculation is of interest since prices are linked to fast moving market variables. We focus on the pricing of a single underlying …
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Gaussian Process Regression for Option Pricing and Hedging
… to investigate the accuracy and efficiency of Gaussian process regression (GPR) compared to traditional quantitative pricing algorithms. The GPR algorithm is applied to pricing a down-and-out barrier call option. Notably, Crepey and Dixon ´ (2019) propose an alternative method for computing the …
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Applications of Gaussian Process Regression to the Pricing and Hedging of Exotic Derivatives
… The purpose of this research is to apply the Gaussian Process Regression (GPR) method to the pricing and hedging of exotic options under the Black-Scholes and Heston model. GPR is a supervised machine learning technique which makes use of a training set to train an algorithm so that it makes …
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Reduced-space Gaussian process regression forecast for nonlinear dynamical systems
… in a reduced-order subspace of interest using Gaussian Process Regression (GPR). GPR simultaneously allows for the reconstruction of the vector field, as well as the estimation of the local uncertainty. The latter is due to i) the local interpolation error and ii) due to the truncation of the …
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Real-Time Motion Prediction for Efficient Human-Robot Collaboration
… form of neural network-based architecture or use regression models offline to fit hyper-parameters in the hope of capturing a model encompassing human motion. While these methods provide good initial results, they are missing out on leveraging well-studied human body kinematic models as well as …
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Robust and Data-Efficient Metamodel-Based Approaches for Online Analysis of Time-Dependent Systems
… impractical. As a popular metamodeling method, Gaussian process regression (GPR), has been successfully applied to analyses of various engineering systems. However, GPR-based metamodeling for time-dependent systems (TDSs) is especially challenging due to three reasons. First, TDSs require an …
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Voxelized Atomic Structure Framework for Atomistic Modeling of Multifunctional Materials
… and correlated to physical properties by Gaussian process regression (GPR). The uncertainty quantification inherently provided by GPR is utilized to implement an active learning strategy based on Bayesian experiment design, which minimizes the number of computationally expensive physics …
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Towards Efficient Hydraulic Manipulator Control using Learning-Based Model Predictive Control
… Recently, research has explored the use of Gaussian Process Regression (GPR) to enhance the modelling of manipulator dynamics, ultimately enabling more accurate control. Most control solutions have employed reactive control techniques, such as Inverse Dynamics controllers, for manipulator …
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Diffusion-weighted MRI of skeletal muscle: Estimation of microstructural parameters
… is addressed through the development of a Gaussian process regression (GPR) model. The GPR model produces a mean estimate as well as a confidence interval for each voxel, allowing confidence bounds to be developed for the prediction of any microstructural parameter. The GPR model is shown …
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Solar power forecasting using Gaussian process regression
… The main goal of this thesis is to explore how Gaussian process predicting frameworks can be developed and used to predict global horiz0ontal irra- diance. Data on Global horizontal irrandiance and some weather variables collected from various meterological stations were made available through …
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Fatigue reliability assessment of co-located offshore wind and wave energy systems
… damage variation with sea state. We apply Gaussian process regression (GPR) for this purpose and propose an adaptive (learning) approach to improve the surrogate model’s accuracy. The three studies taken together all address fatigue damage assessment of offshore renewable energy systems.
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Virtual metrology for plasma etch processes.
Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis …
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Multiscale modelling of woven and knitted fabric membranes
… manufacturing greatly advanced the manufacturing processes of textiles. In this work, we consider two branches of technical fabrics, namely plain-woven and weft-knitted. Multiscale modelling is the tool of choice for homogenising periodic structures and has been used extensively to model and …
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Accuracy Improvement in Robotic Milling Through Data-Driven Modelling and Control
… First, a data-driven modeling approach utilizing Gaussian Process Regression (GPR) of data acquired from modal impact hammer experiments to predict the modal parameters of a 6-dof industrial robot as a function of its arm configuration is presented. The GPR model is found to be capable of …
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Onshore wind farm battery energy storage systems optimisation
… (NN)-based inverse model, Bayesian optimisation, Gaussian Process Regression (GPR), and Reinforcement Particle Swarm Optimisation (RPSO) to enhance wind energy production while providing accurate estimates of system performance. The study further introduces hybrid forecasting models that combine …
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Statistical modeling of aircraft engine fuel burn
… data (including trajectory data) are generally processed in order to generate the inputs needed by BADA, which then provides an estimate of the fuel flow rate and fuel burn. Although a versatile tool that covers a large number of aircraft types, BADA makes several assumptions that are not …
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Transferable Coarse-Grained Models: From Hydrocarbons to Polymers, and Backmapped by Machine Learning
… neural networks (ANN), k-nearest neighbor (kNN), gaussian process regression (GPR), and random forest (RF) were developed to improve the accuracy of the backmapped all-atom structures. These optimized four ML models showed R2 scores of more than 0.99 when testing the backmapping against four …
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Water Quality Control in Distribution Systems: Bayesian Optimization & Physics-Informed Machine Learning
… (PDEs) to simulate the underlying physical processes that govern chlorine transport and decay in the WDS. This dissertation aims to address these key challenges by developing innovative WQ prediction and control frameworks to enable efficient and sustainable chlorine residual management in …
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