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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 40 for “"Uncertainty Quantification (UQ)"”.
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Assessing the applicability of the ASME V&V20 standard for uncertainty quantification of CFD in nuclear systems fluid modeling
… of M&S research to the U.S. nuclear Industry. UncertaInty QuantIfIcatIon (UQ) represents a fundamental area of research necessary to expand the applIcatIon of M&S Into nuclear Industry, but the fIeld Is stIll not mature, and no general consensus exIsts on current UQ methods. In thIs study, the …
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Physics-informed Machine Learning with Uncertainty Quantification
… available in the form of physics supervision. Uncertainty quantification (UQ) is an important goal in many scientific use-cases, where the obtaining reliable ML model predictions and accessing the potential risks associated with them is crucial. In this thesis, we propose novel methodologies in …
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Quantifying time-dependent uncertainty in the BEAVRS benchmark using time series analysis
… However, in order to address the issue of BEAVRS uncertainty quantification (UQ) of Uranium-235 fission reaction rate data, this thesis proposes a new method for measuring uncertainty that goes beyond merely conducting statistical analysis of multiple measurements at one given point in time. …
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Efficient Uncertainty Quantification of Large Language Models
… domains such as healthcare, finance, and law. Uncertainty Quantification (UQ) is essential for assessing LLM outputs and ensuring trust. However, existing UQ methods for LLMs face challenges: high computational costs, difficulties in handling unstructured outputs, and limited generalizability. …
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Uncertainty Quantification in Security Aware Data Pipelines
… capabilities aimed at estimating the uncertainty of computations with constant monitoring of trends in shifts in data at every pipeline stage. The proposed framework integrates uncertainty quantification (UQ), data provenance tracking, sensitivity analysis, and tunable alerts to …
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Uncertainty quantification and calibration in nuclear safety codes using Gaussian process active learning
Inverse problems and inverse uncertainty quantification (UQ) are challenging issues when dealing with complex and highly non-linear functions. Methods have been developed to decrease the computational burden by using the Gaussian Process (GP) emulator model framework to approximate the input-output …
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Scalable Surrogates for Counts and Computer Experiments
… solar system. Providing estimates and associated uncertainty quantification (UQ) of the rate at which ENAs are generated is vital to theory development and validation. Gaussian processes (GPs) constitute an excellent nonparametric regression tool that can provide accurate out-of-sample prediction …
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Deep Gaussian Process Surrogates for Computer Experiments
… and reliability analysis - demand broader uncertainty quantification (UQ). I prioritize UQ through full posterior integration in a Bayesian scheme, hinging on elliptical slice sampling of latent layers. I demonstrate how my DGP's non-stationary flexibility, combined with appropriate UQ, …
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Robust and Data-Driven Uncertainty Quantification Methods as Real-Time Decision Support in Data-Driven Models
… corruption, bias, limited interpretability, and uncertainty misrepresentation—which can compromise their reliability. Propagating uncertainties from sources like model parameters and input features is crucial in data-driven models to ensure trustworthy predictions and informed decisions. …
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High dimensional uncertainty propagation for hypersonic flows and entry propagation
… this work, two approaches for a high dimensional uncertainty quantification (UQ) are developed. The first approach performs a single-fidelity non-intrusive forward UQ, while the second one performs a multi fidelity UQ, as an extension of the first approach. Both methods are focused on real …
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Epistemic Uncertainty Quantification in Scientific Models
<p>In the field of uncertainty quantification (UQ), epistemic uncertainty often refers to the kind of uncertainty whose complete probabilistic description is not available, largely due to our lack of knowledge about the uncertainty. Quantification of the impacts of epistemic uncertainty is …
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Model Reduction and Domain Decomposition Methods for Uncertainty Quantification
… focuses on acceleration techniques for Uncertainty Quantification (UQ). The manuscript is divided into five chapters. Chapter 1 provides an introduction and a brief summary of Chapters 2, 3, and 4. Chapter 2 introduces a model reduction strategy that is used in the context of elasticity …
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Similarity-Augmented Prediction Methods for Neural Machine Translation
… e.g. beam search, and probability-based uncertainty quantification (UQ), e.g. Shannon entropy. In this thesis, we study a class of methods which measure semantic similarities between elements in the LM output distribution, which we call similarity-augmented prediction methods. The most …
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Enhancing Robustness of Neural Network Interatomic Potentials through Sampling Methods and Uncertainty Quantification
… to address these challenges through analysis of uncertainty quantification (UQ) techniques, introduction of novel data sampling strategies, and development of structural similarity analysis algorithm to extract physical insights from diverse data sets. First, we examine the efficacy of UQ for …
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Machine Learning-Driven Uncertainty Quantification and Parameter Analysis in Fire Risk Assessment for Nuclear Power Plants
… neural networks and tree-based algorithms, with uncertainty quantification (UQ) techniques to enhance fire modeling and risk assessment in NPPs. The framework is applied to electrical enclosure cabinets and spill fires that represent about 50% of challenging fire scenarios in NPPs. By leveraging …
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Uncertainty quantification in the dynamic analysis of offshore structures
Consideration for uncertainty is critical in problems associated with structural dynamics, especially in the offshore environment. Deterministic solutions are often insufficient to achieve confidence in computational results and for use in design. In uncertainty quantification (UQ) for problems …
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Stochastic modeling and uncertainty quantification in microelectromechanical systems
Uncertainty quantification (UQ) has become a necessary step in the design of most modern engineering systems due to the need to create robust devices that can tolerate variations in the manufacturing process or in the operating environment. These variations or uncertainties can be represented by …
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Uncertainty quantification of unconventional reservoirs using assisted history matching methods
… of unconventional reservoirs is characterization uncertainty. Assisted History Matching (AHM) methods provide attractive means for uncertainty quantification (UQ), because they yield an ensemble of qualifying models instead of a single candidate. Here we integrate embedded discrete fracture model …
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Modeling transport phenomena and uncertainty quantification in solidification processes
… that cannot be observed experimentally. However, uncertainty in model inputs cause uncertainty in results and those insights. The analysis of model assumptions and probable input variability on the level of uncertainty in model predictions has not been calculated in solidification modeling as …
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Application of data-driven methods in nuclear fuel performance analysis
… importance ranking, benefiting the subsequent uncertainty quantification (UQ). To enhance the predictability, a novel Bayesian inference framework is introduced to efficiently calibrate the expensive high fidelity tools, possibly without resorting to approximate surrogate methods. The …
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