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 347 for “"Uncertainty quantification"”.
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Uncertainty quantification in noisy networks
… capabilities that we still lack is quantifying uncertainty through propagation of network error. We conduct basic research laying statistical foundations for uncertainty quantification of this type, within a handful of key paradigms, focusing on problems ranging from epidemics to experiments on …
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Rheological inferences with uncertainty quantification
… protocol. We remind the community that an honest uncertainty quantification of fit parameter estimates requires fitting SAOS+MAOS simultaneously. Even then there are subjective choices in fitting, particularly for SAOS data. We demonstrate that fitting with data uncertainty weighted least squares …
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Uncertainty quantification in ocean state estimation
Quantifying uncertainty and error bounds is a key outstanding challenge in ocean state estimation and climate research. It is particularly difficult due to the large dimensionality of this nonlinear estimation problem and the number of uncertain variables involved. The “Estimating the Circulation …
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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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M/NEM devices and uncertainty quantification
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01
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Uncertainty Quantification in ocean state estimation
Quantifying uncertainty and error bounds is a key outstanding challenge in ocean state estimation and climate research. It is particularly difficult due to the large dimensionality of this nonlinear estimation problem and the number of uncertain variables involved. The "Estimating the Circulation …
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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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Uncertainty quantification and calibration of physical models
… and simplification of the physical model. Quantification of uncertainty of such an ecosystem model is computationally very heavy. Bayesian calibration method has been used as an efficient way to calibrate and quantify uncertainties of the computer models. In this work, I develop a new …
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Multimodal Probabilistic Inference for Robust Uncertainty Quantification
… a pressing need to equip models with a notion of uncertainty to understand their failure modes and detect when their decisions cannot be used or require intervention. Uncertainty quantification is thus crucial for ML systems to work consistently on real-world data and fail loudly when they …
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Distribution-free uncertainty quantification for deep learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Distribution-free uncertainty quantification for deep learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Uncertainty quantification for pavement life-cycle stages
… fuel due to structural rolling resistance (SRR). Uncertainty of pavement roughness has significant impact on the energy and emission output of the pavement-vehicle system depending on the precision level of the model used, input variabilities, and prior knowledge of the model parameters. When …
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Trustworthy Learning and Uncertainty Quantification under Constraints
… efficiency, interpretability, robustness, and uncertainty quantification. This thesis focus on proposing novel techniques for critical requirements of trustworthy machine learning models, including uncertainty quantification, computational efficiency, and privacy preservation. Within this …
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Essays on Algorithmic Learning and Uncertainty Quantification
The thesis consists of three essays. The first, titled “Localization, Convexity, and Star Aggregation,” develops new analytical tools based upon the offset Rademacher complexity for studying stochastic optimization in non-convex domains, including statistical prediction and model aggregation …
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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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Stochastic Computer Model Calibration and Uncertainty Quantification
… of stochastic computer model calibration and uncertainty quantification. Simulation models are widely used in studying physical systems, which are often represented by a set of mathematical equations. Inference on true physical system (unobserved or partially observed) is drawn based on the …
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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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Data-Driven Polynomial Chaos Expansions for Uncertainty Quantification
… how system inputs affect a system output's uncertainty is essential to improve system outputs such as quality and productivity. Variance-based sensitivity analysis, which is widely used for uncertainty quantification, characterizes how the output variance is propagated from inputs. To …
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Stochastic methods for uncertainty quantification in radiation transport
… (KL) expansions, is investigated for uncertainty quantification in radiation transport. The gPC represents second-order random processes in terms of an expansion of orthogonal polynomials of random variables. The KL expansion is a Fourier-type expansion that represents a second-order …
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