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 150 for “"Surrogate Model"”.
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Surrogate Model Optimisation for PWR Fuel Management
… to investigate. In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and …
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Development of Surrogate Model for FEM Error Prediction using Deep Learning
… is a proof-of-concept study to develop a surrogate model, using deep learning (DL), to predict solution error for a given model with a given mesh. For this research, we have taken the von Mises stress contours and have predicted two different types of error indicators contours, namely (i) …
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Using Machine Learning to Generate a Surrogate Model for Plasma-Surface Interactions
… in laboratory plasmas, but too complicatedto be modelled directly in plasma simulations. However, plasma surface interactions can be modelled by Transport and Range of Ions in Matter (TRIM) simulations based on a binary collision approximation of energetic ions impinging on a stationary material. …
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Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation.
… optic, pressure point analysis and numerical modelling, have been proposed during the last decades. One major issue of these methods is distinguishing the leak signal without giving false alarms. Considering that the data obtained by these traditional methods are digital in nature, the machine …
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Surrogate Model Assisted Nested Simulation with Applications to Variable Annuity Portfolio Valuation and Hedging
… In this thesis, we incorporate the idea of surrogate modelling to the nested simulation algorithm such that all of the input dimensions are reduced. The surrogate models act as proxies to approximate the input/output relationships. Since only a few input points are needed in order to …
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Investigating the Maximal Coverage by Point-based Surrogate Model for Spatial Facility Location Problem
… discrete points, and then solve the point-based model as a conventional facility location problem (FLP) according to a surrogate model. Solution performance is measured in terms of the percentage of continuous space actually covered in the original SFLP. In this dissertation I explore this …
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Development of a Lung Surrogate Model for Assessing Biomechanical Responses to Underwater Explosions (UNDEX)
… objective of this research is to develop a surrogate modeling framework using engineering materials to investigate the biomechanical response of lung tissue during UNDEX events. A representative lung surrogate was designed to mimic the mechanical behavior of human lungs, utilizing …
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A Data Driven Modeling Approach for Store Distributed Load and Trajectory Prediction
… this objective is available through data-driven surrogate modeling of store distributed loads. This dissertation investigates the practicality of applying various data-driven modeling techniques to the field of store separation. These modeling methods will be applied to four demonstration …
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MACHINE LEARNING FOR CONSTITUTIVE MODELLING
… learning (ML) techniques have been applied as surrogate models in the micro scale of multi-scale analysis to reduce the computational cost. These surrogate models provide the constitutive law of the micro scale simulations after a training process, called off-line learning. For history …
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Computational Modeling of Thoracic Injury Response due to Impact of a Small Unmanned Aerial System (SUAS)
<p>This study aims to establish a computational model that will predict the injury response after a sUAS impacts the thorax. A rotary and fixed winged sUAS were chosen for analysis. A vast number of numerical simulations were carried out with varying masses, impact velocities, and impact angles. …
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A physics-informed neural network modelling methodology to analyse integrated thermofluid systems
… learning methods. This study explores a PINN modelling methodology to analyse steady-state integrated thermofluid systems based on the mass, energy, and momentum balance equations, combined with the relevant component characteristics and fluid property relationships. The PINN methodology is …
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Predictive haemodynamics of the human carotid artery
… parametric computer aided design (CAD) model of the human carotid bifurcation. A Bayesian surrogate modelling technique is proposed and discrete locations in the CAD model are taken as random parameters to form inputs for the surrogate model. A metric, maximal wall shear stress (MWSS) is …
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An integrated furnace co-simulation methodology based on a reduced order CFD approach
An integrated thermofluid modelling methodology for pulverised fuel fired utility-scale boilers that is computationally inexpensive, fast, and sufficiently accurate would be valuable in an industrial setting. Such a model would enable boiler operators to investigate a range of off-design operating …
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Hybrid adaptive sequential sampling for reliability-based design optimization
… and optimization of complex engineering systems, surrogate models can be used to replace expensive physical models. One of the critical tasks of employing surrogate models for system design optimization is to develop an accurate surrogate model cost-effectively. In this thesis, a new hybrid …
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Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
… of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, including carbon nanotube polymer blends, silver nanoparticles, lead-halide perovskites, as well as additively manufactured polymer structures and …
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Quantification of Elastic Incompatibilities at Triple Junctions via Physics-Based Surrogate Models
… the practical use of polycrystalline materials. Surrogate models based on machine learning methods have gained broad popularity due to their ability to furnish a functional, albeit approximate, description of complex phenomena. The goal of this thesis is to predict quantitative metrics of …
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A Graph Neural Network for pairwise surrogate modeling in population-based algorithms with tournament selection
… intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the …
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Development of a process modelling methodology and condition monitoring platform for air-cooled condensers
… ambient conditions. This study focuses on modelling a utility-scale ACC system at steady-state conditions applying a 1-D network modelling approach and using a component-level discretization approach. This approach allowed for each cell to be modelled individually, accounting for steam duct …
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OPISA optoelectronics packaging interfaces for sub-micron alignment
… the dissemination of this work. Finite element models were developed and used with design of experiments (DoE) statistical technique. This was used to investigate how the key process parameters influence the resultant stress in the optical fibre and to develop a predictive surrogate model. The …
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