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 8 of 8 for “"Gaussian process surrogate"”.
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Real-time Autonomy and Maneuvering Simulation of an Unmanned Underwater Vehicle Near a Moving Submarine Using Actively Sampled Gaussian Process Surrogate Models
… learning framework based on actively sampled Gaussian Process (GP) regression is developed to create a reduced-order model (ROM) that predicts the hydrodynamic interactions in real time using a minimum number of expensive simulations. We show that the introduced active learning framework, …
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Multi-fidelity data fusion for the design of multidisciplinary systems under uncertainty
… of high-fidelity data into a conceptual design process. The methodology is based upon a fidelity weighted combination of Gaussian Process surrogate models that takes into account both the quality of the Gaussian Process approximation and the confidence of the designer in the disciplinary model …
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Variational Inference and Probabilistic Models for Parametric Partial Differential Equations
… contribution lies in creating active learning surrogates for Bayesian inverse problems called Active learning projected surrogates – SVGD. Here we leverage Stein variational gradient descent methods to move clusters of particles through the information given by an active learning Gaussian …
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Bayesian robust optimisation of buckling loads of trusses with random imperfections
… we apply the Bayesian optimisation with a Gaussian process surrogate model and the iterative domain shrinkage scheme for the described robust optimisation problem. Compared to traditional gradient-based optimisation approaches, Bayesian optimisation is exceptionally well suited for …
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Approximate Bayesian Modeling with Embedded Gaussian Processes
… inference methods. We propose the embedded Gaussian process framework to address these challenges. The embedded GP model captures the uncertainty of complex physical models and incorporates it in posterior inference where a joint distribution of all uncertain quantities, including the …
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Towards a psychological science of neural network behaviour
… machine learning setting, through the use of a Gaussian Process surrogate and Bayesian experimental design. We demonstrate the utility of the machine learning multiverse through two case studies, one on the relative merit of adaptive versus non-adaptive gradient-based optimisers, and the other …
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Precision Aggregated Local Models
Large scale Gaussian process (GP) regression is infeasible for larger data sets due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies in recent literature focus on divide-and-conquer, e.g., partitioning into sub-problems and inducing functional …
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Scalable Estimation and Testing for Complex, High-Dimensional Data
… mutation rates of a generalized birth-death process based on fluctuation experimental data and estimating the parameters of targets based on foliage echoes. The second part focuses on functional testing. We consider using multiple testing in basis-space via p-value guided compression. Our …