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 6 of 6 for “"Mixture density networks"”.
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Finescale abyssal turbulence: sources and modeling
… a probabilistic finescale parameterization using mixture density networks (MDNs). A boundary layer, formed by the interaction of heaving isopycnals by the tide and viscous/adiabatic boundary conditions, is investigated through direct numerical simulations (DNS) and Floquet analysis. Turbulence is …
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Improved uncertainty estimates for geophysical parameter retrieval
… Current retrieval algorithms based on neural networks are superior in accuracy and speed compared to physics-based algorithms like iterated minimum variance (IMV). However, they do not have any form of error estimation, unlike IMV. This thesis examines the suitability of several different …
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Real-time Delay Prediction and Quality of Service Assurance for Traffic Management in Service Systems
… study delay distribution prediction in service networks and propose novel QoS assurance methods which target the mentioned factors. In particular, the proposed methods require no knowledge of the system model or parameters, which is an important feature for real-world applications. We first …
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Deep Learning For Surrogate Modeling And Uncertainty Quantification In Science & Engineering
… it introduces Neural Epistemic Operator Networks (NEON), which integrate the Epistemic Neural Network framework with neural operators for learning function-to-function mappings. This design enables scalable and well-calibrated epistemic uncertainty estimates using a single operator …
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A pragmatic Bayesian approach to wind field retrieval
… directly from the scatterometer data by using mixture density networks, a principled method to model multi-modal conditional probability density functions. The complexity of the mapping and the structure of the conditional probability density function are investigated. A hybrid mixture density …
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Application of probabilistic deep learning models to simulate thermal power plant processes
… entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, …