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 26 for “"Optimal Experimental Design"”.
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Parameter and topology uncertainty for optimal experimental design
… simulation, quantification of uncertainty, and optimal experimental design for reducing uncertainty. We applied a previously described algorithm for choosing optimal experiments for reducing parameter uncertainty as estimated by the Fisher information matrix. We found, using a computational …
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Optimal experimental design applied to DC resistivity problems
The systematic design of experiments to optimally query physical systems through manipulation of the data acquisition strategy is termed optimal experimental design (OED). This dissertation introduces the state-of-the-art in OED theory and presents a new design methodology, which is demonstrated by …
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Numerical approaches for sequential Bayesian optimal experimental design
Experimental data play a crucial role in developing and refining models of physical systems. Some experiments can be more valuable than others, however. Well-chosen experiments can save substantial resources, and hence optimal experimental design (OED) seeks to quantify and maximize the value of …
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Optimal Experimental Design for Poisson Impaired Reproduction Studies
… has been focused on the development of efficient experimental designs for impaired reproduction studies. This research concentrates on two areas: 1) the use of Bayesian techniques to make single regressor designs robust to parameter misspecification and 2) the extension of design optimality …
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Parameter estimation and optimal experimental design in flow reactors
… measurements. We introduce methods for the optimal experimental design of experiments in the context of reactive flows modeled by partial differential equations.
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BAYESIAN SEQUENTIAL OPTIMAL EXPERIMENTAL DESIGN FOR INVERSE PROBLEMS USING DEEP REINFORCEMENT LEARNING
… a comprehensive study on Bayesian sequential optimal experimental design techniquesapplied to inverse problems. We transform the Bayesian sequential optimal experimental design problem into a reinforcement learning problem to gauge the power of recent deep reinforcement learning algorithms …
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On the advancement of optimal experimental design with applications to infectious diseases.
In this thesis, we investigate the optimal experimental design of some common biological experiments. The theory of optimal experimental design is a statistical tool that allows us to determine the optimal experimental protocol to gain the most information about a particular process, given …
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New tools for Bayesian optimal experimental design and kernel-based generative modeling
… problems in statistics and machine learning: optimal experimental design and generative modeling. Optimal experimental design (OED) is important to model development for science and engineering applications and beyond, especially when only a small number of observations can be taken or …
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A combinatorial approach to goal-oriented optimal Bayesian experimental design
Optimal experimental design plays an important role in science and engineering. In many situations, we have many observations but only few of them can be selected due to limited resources. We then need to decide which ones to select based on our goal. In this thesis, we study the Bayesian linear …
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Numerical methods for optimization problems in water flow and reactive solute transport processes of xenobiotics in soils
… 1. Modeling of parameter estimation problems and optimal experimental design problems: Mathematical modeling of water and solute transport processes in the unsaturated zone leads to instationary partial differential equations (PDEs) coupled with nonlinear ordinary differential equations (ODEs). 2. …
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An exercise in selecting low-cost air quality sensor placements within an urban environment
… pollution. The question then arises, what is the optimal configuration of low-cost sensors to measure air pollution within an urban environment? Due to the large number of potential locations in which to measure data, there are difficulties in defining where to place a limited number of sensors. …
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Experimental design and analysis for high-parameter spatial omics
… biology to propose a general framework for experimental design in spatial omics, introduce an approximate generative model of tissue structure, and demonstrate methods for semantic segmentation and community detection in spatial omics. Finally, I share an outlook on how, when considered …
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Optimal Bayesian experimental design in the presence of model error
The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction. We propose an information theoretic framework and algorithms for robust optimal experimental design with simulation-based models, with the goal of maximizing information gain …
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Accelerated Bayesian experimental design for chemical kinetic models
The optimal selection of experimental conditions is essential in maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. A general Bayesian framework for optimal experimental design with nonlinear …
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Hydrodynamic Interactions of an Unmanned Underwater Vehicle Operating in Close Proximity to a Moving Submarine
… process regression model was used to perform an optimal experimental design and map the resulting hydrodynamic interactions based on the UUVs longitudinal position, lateral position, speed, heading angle, UUV diameter, and UUV length. The model was validated using an out of sampling method and …
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Novel Application of Nondestructive Testing to Evaluate Anomalous Conditions in Drilled Shafts and the Geologic Materials Underlying Their Excavations
… are warranted to ensure these foundations meet design specifications, particularly with respect to their structural integrity and geotechnical capacity. Due to their inaccessibility, non-destructive testing (NDT) techniques have received much attention for drilled shaft quality control. However, …
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Accounting for Computational Expenditures in Bayesian Experimental Design
… for the data collection process is known as experimental design. The Bayesian optimal experimental design (BOED) formulation uses Bayesian inference to update beliefs after observing data and optimizes a utility function – most commonly mutual information – and is computationally challenging. …
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Damage Detection and Sensor Placement Strategies for Structures Under Frequency-Domain Dynamics
… many methods presuppose that sufficiently many experimental data points are obtained. As these monitoring schemes are applied to higher risk, more detailed, and aging structures, a key challenge is the gathering of sufficient information through sensors such that modern damage estimation …
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