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Artigos de revistas sobre o tema "Predictive uncertainty quantification"

Autor: Grafiati

Publicado: 5 de outubro de 2024

Última modificação: 31 de julho de 2025

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1

Cacuci, Dan Gabriel. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems." Energies 15, no. 17 (2022): 6379. http://dx.doi.org/10.3390/en15176379.

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The Special Issue “Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems” comprises nine articles that present important applications of concepts for performing sensitivity analyses and uncertainty quantifications of models of nuclear energy systems [...]

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Csillag, Daniel, Lucas Monteiro Paes, Thiago Ramos, et al. "AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15494–502. http://dx.doi.org/10.1609/aaai.v37i13.26837.

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Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages deep learning and conformal prediction to output fast and accurate volume estimates and segmentation masks from fetal MRIs with Dice coefficient over 0.9. Also, we make available a novel, curated dataset for fetal MRIs with 853 exams and benchmark the performance of many recent deep learning architectures. In addition, we introduce a conforma

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Lew, Jiann-Shiun, and Jer-Nan Juang. "Robust Generalized Predictive Control with Uncertainty Quantification." Journal of Guidance, Control, and Dynamics 35, no. 3 (2012): 930–37. http://dx.doi.org/10.2514/1.54510.

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Karimi, Hamed, and Reza Samavi. "Quantifying Deep Learning Model Uncertainty in Conformal Prediction." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 142–48. http://dx.doi.org/10.1609/aaaiss.v1i1.27492.

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Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoret

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Serenko, I. A., Y. V. Dorn, S. R. Singh, and A. V. Kornaev. "Room for Uncertainty in Remaining Useful Life Estimation for Turbofan Jet Engines." Nelineinaya Dinamika 20, no. 5 (2024): 933–43. https://doi.org/10.20537/nd241218.

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This work addresses uncertainty quantification in machine learning, treating it as a hidden parameter of the model that estimates variance in training data, thereby enhancing the interpretability of predictive models. By predicting both the target value and the certainty of the prediction, combined with deep ensembling to study model uncertainty, the proposed method aims to increase model accuracy. The approach was applied to the well-known problem of Remaining Useful Life (RUL) estimation for turbofan jet engines using NASA’s dataset. The method demonstrated competitive results compared to ot

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Akitaya, Kento, and Masaatsu Aichi. "Land Subsidence Model Inversion with the Estimation of Both Model Parameter Uncertainty and Predictive Uncertainty Using an Evolutionary-Based Data Assimilation (EDA) and Ensemble Model Output Statistics (EMOS)." Water 16, no. 3 (2024): 423. http://dx.doi.org/10.3390/w16030423.

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The nonlinearity nature of land subsidence and limited observations cause premature convergence in typical data assimilation methods, leading to both underestimation and miscalculation of uncertainty in model parameters and prediction. This study focuses on a promising approach, the combination of evolutionary-based data assimilation (EDA) and ensemble model output statistics (EMOS), to investigate its performance in land subsidence modeling using EDA with a smoothing approach for parameter uncertainty quantification and EMOS for predictive uncertainty quantification. The methodology was teste

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Sriprasert, Soraida, and Patchanok Srisuradetchai. "Multi-K KNN regression with bootstrap aggregation: Accurate predictions and alternative prediction intervals." Edelweiss Applied Science and Technology 9, no. 5 (2025): 2750–64. https://doi.org/10.55214/25768484.v9i5.7589.

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The k-nearest neighbors (KNN) algorithm is widely recognized for its simplicity and flexibility in modeling complex, non-linear relationships; however, standard KNN regression does not inherently provide prediction intervals (PIs), presenting a persistent challenge for uncertainty quantification. This study introduces a bootstrap-based multi-K approach specifically designed to construct robust prediction intervals in KNN regression. By systematically aggregating predictions across multiple neighborhood sizes through ensemble techniques and bootstrap resampling, the method effectively quantifie

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Chala, Ayele Tesema, and Richard Ray. "Uncertainty Quantification in Shear Wave Velocity Predictions: Integrating Explainable Machine Learning and Bayesian Inference." Applied Sciences 15, no. 3 (2025): 1409. https://doi.org/10.3390/app15031409.

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The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized linear model (GLM) to enhance both predictive accuracy and uncertainty quantification in Vs prediction. The study utilizes an Extreme Gradient Boosting (XGBoost) algorithm coupled with Shapley Additive Explanations (SHAPs) and partial dependency analysis to id

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Ayed, Safa Ben, Roozbeh Sadeghian Broujeny, and Rachid Tahar Hamza. "Remaining Useful Life Prediction with Uncertainty Quantification Using Evidential Deep Learning." Journal of Artificial Intelligence and Soft Computing Research 15, no. 1 (2024): 37–55. https://doi.org/10.2478/jaiscr-2025-0003.

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Abstract Predictive Maintenance presents an important and challenging task in Industry 4.0. It aims to prevent premature failures and reduce costs by avoiding unnecessary maintenance tasks. This involves estimating the Remaining Useful Life (RUL), which provides critical information for decision makers and planners of future maintenance activities. However, RUL prediction is not simple due to the imperfections in monitoring data, making effective Predictive Maintenance challenging. To address this issue, this article proposes an Evidential Deep Learning (EDL) based method to predict the RUL an

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Plesner, Andreas, Allan P. Engsig-Karup, and Hans True. "Detecting Railway Track Irregularities with Data-driven Uncertainty Quantification." Highlights of Vehicles 3, no. 1 (2025): 1–14. https://doi.org/10.54175/hveh3010001.

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This study addresses the critical challenge of assessing railway track irregularities using advanced machine learning techniques, specifically convolutional neural networks (CNNs) and conformal prediction. Leveraging high-fidelity sensor data from high-speed trains, we propose a novel CNN model that significantly outperforms state-of-the-art results in predicting track irregularities. Our CNN architecture, optimized through extensive hyperparameter tuning, comprises multiple convolutional layers with batch normalization, Exponential Linear Unit (ELU) activation functions, and dropout regulariz

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Singh, Rishabh, and Jose C. Principe. "Toward a Kernel-Based Uncertainty Decomposition Framework for Data and Models." Neural Computation 33, no. 5 (2021): 1164–98. http://dx.doi.org/10.1162/neco_a_01372.

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Abstract This letter introduces a new framework for quantifying predictive uncertainty for both data and models that relies on projecting the data into a gaussian reproducing kernel Hilbert space (RKHS) and transforming the data probability density function (PDF) in a way that quantifies the flow of its gradient as a topological potential field (quantified at all points in the sample space). This enables the decomposition of the PDF gradient flow by formulating it as a moment decomposition problem using operators from quantum physics, specifically Schrödinger's formulation. We experimentally s

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Doherty, Conor T., Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan. "A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature." Geoscientific Model Development 18, no. 10 (2025): 3003–16. https://doi.org/10.5194/gmd-18-3003-2025.

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Abstract. Uncertainty is inherent in gridded meteorological data, but this fact is often overlooked when data products do not provide a quantitative description of prediction uncertainty. This paper describes, applies, and evaluates a method for quantifying prediction uncertainty in spatially interpolated estimates of meteorological variables. The approach presented here, which we will refer to as DNK for “detrend, normal score, krige”, uses established methods from geostatistics to produce not only point estimates (i.e., a single number) but also predictive distributions for each location. Pr

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Chen, Peng, and Nicholas Zabaras. "Adaptive Locally Weighted Projection Regression Method for Uncertainty Quantification." Communications in Computational Physics 14, no. 4 (2013): 851–78. http://dx.doi.org/10.4208/cicp.060712.281212a.

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AbstractWe develop an efficient, adaptive locally weighted projection regression (ALWPR) framework for uncertainty quantification (UQ) of systems governed by ordinary and partial differential equations. The algorithm adaptively selects the new input points with the largest predictive variance and decides when and where to add new local models. It effectively learns the local features and accurately quantifies the uncertainty in the prediction of the statistics. The developed methodology provides predictions and confidence intervals at any query input and can deal with multi-output cases. Numer

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Shi, Yuanjie. "Reliable Uncertainty Quantification in Machine Learning via Conformal Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29299–300. https://doi.org/10.1609/aaai.v39i28.35227.

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Deploying machine learning (ML) models in high-stakes domains such as healthcare and autonomous systems requires reliable uncertainty quantification (UQ) to ensure safe and accurate decision-making. Conformal prediction (CP) offers a robust, distribution-agnostic framework for UQ, providing valid prediction sets that guarantee a specified coverage probability. However, existing CP methods are often limited by assumptions that are violated in real-world scenarios, such as non-i.i.d. data, and by a lack of integration with modern machine learning workflows, particularly in large generative model

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Farzana, Walia, Megan A. Witherow, Ahmed Temtam, et al. "24 Key brain region identification in obesity prediction with structural MRI and probabilistic uncertainty aware model." Journal of Clinical and Translational Science 9, s1 (2025): 9. https://doi.org/10.1017/cts.2024.715.

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Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patie

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Omagbon, Jericho, John Doherty, Angus Yeh, et al. "Case studies of predictive uncertainty quantification for geothermal models." Geothermics 97 (December 2021): 102263. http://dx.doi.org/10.1016/j.geothermics.2021.102263.

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Nitschke, C. T., P. Cinnella, D. Lucor, and J. C. Chassaing. "Model-form and predictive uncertainty quantification in linear aeroelasticity." Journal of Fluids and Structures 73 (August 2017): 137–61. http://dx.doi.org/10.1016/j.jfluidstructs.2017.05.007.

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Mirzayeva, A., N. A. Slavinskaya, M. Abbasi, J. H. Starcke, W. Li, and M. Frenklach. "Uncertainty Quantification in Chemical Modeling." Eurasian Chemico-Technological Journal 20, no. 1 (2018): 33. http://dx.doi.org/10.18321/ectj706.

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A module of PrIMe automated data-centric infrastructure, Bound-to-Bound Data Collaboration (B2BDC), was used for the analysis of systematic uncertainty and data consistency of the H2/CO reaction model (73/17). In order to achieve this purpose, a dataset of 167 experimental targets (ignition delay time and laminar flame speed) and 55 active model parameters (pre-exponent factors in the Arrhenius form of the reaction rate coefficients) was constructed. Consistency analysis of experimental data from the composed dataset revealed disagreement between models and data. Two consistency measures were

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Albi, Giacomo, Lorenzo Pareschi, and Mattia Zanella. "Uncertainty Quantification in Control Problems for Flocking Models." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/850124.

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The optimal control of flocking models with random inputs is investigated from a numerical point of view. The effect of uncertainty in the interaction parameters is studied for a Cucker-Smale type model using a generalized polynomial chaos (gPC) approach. Numerical evidence of threshold effects in the alignment dynamic due to the random parameters is given. The use of a selective model predictive control permits steering of the system towards the desired state even in unstable regimes.

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Kumar, Bhargava, Tejaswini Kumar, Swapna Nadakuditi, Hitesh Patel, and Karan Gupta. "Comparing Conformal and Quantile Regression for Uncertainty Quantification: An Empirical Investigation." International Journal of Computing and Engineering 5, no. 5 (2024): 1–8. http://dx.doi.org/10.47941/ijce.1925.

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Purpose: This research assesses the efficacy of conformal regression and standard quantile regression in uncertainty quantification for predictive modeling. Quantile regression estimates various quantiles within the conditional distribution, while conformal regression constructs prediction intervals with guaranteed coverage. Methodology: By training models on multiple quantile pairs and varying error rates, the analysis evaluates each method's performance. Findings: Results indicate consistent trends in coverage and prediction interval lengths, with no significant differences in performance. Q

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Zhang, Haofeng. "Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29317–18. https://doi.org/10.1609/aaai.v39i28.35236.

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While advances in machine learning and the expansion of massive datasets have significantly improved predictive accuracy, the translation of these predictions into actionable decisions—alongside a robust understanding of associated risks—remains underexplored. My research focuses on developing methodology and theory in data-driven decision-making and uncertainty quantification that effectively address core data challenges. This paper presents two connected pillars of my research: data-driven contextual optimization, uncertainty quantification and reduction.

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Kayusi, Fredrick, Petros Chavula, Gilbert Lungu, and Hockings Mambwe. "AI-Driven Climate Modeling: Validation and Uncertainty Mapping – Methodologies and Challenges." LatIA 3 (March 25, 2025): 332. https://doi.org/10.62486/latia2025332.

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Climate models are fundamental for predicting future climate conditions and guiding mitigation and adaptation strategies. This study aims to enhance the accuracy and reliability of climate modeling by integrating artificial intelligence (AI) techniques for validation and uncertainty mapping. AI-driven approaches, including machine learning-based parameterization, ensemble simulations, and probabilistic modeling, offer improvements in model precision, quality assurance, and uncertainty quantification. A systematic review methodology was applied, selecting peer-reviewed studies from 2000 to 2023

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Gorle, Catherine. "Improving the predictive capability of building simulations using uncertainty quantification." Science and Technology for the Built Environment 28, no. 5 (2022): 575–76. http://dx.doi.org/10.1080/23744731.2022.2079261.

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Gerber, Eric A. E., and Bruce A. Craig. "A mixed effects multinomial logistic-normal model for forecasting baseball performance." Journal of Quantitative Analysis in Sports 17, no. 3 (2021): 221–39. http://dx.doi.org/10.1515/jqas-2020-0007.

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Abstract Prediction of player performance is a key component in the construction of baseball team rosters. As a result, most prediction models are the proprietary property of team or industrial sports entities, and little is known about them. Of those models that have been published, the main focus has been to separately model each outcome with nearly no emphasis on uncertainty quantification. This research introduces a joint modeling approach to predict seasonal plate appearance outcome vectors using a mixed-effects multinomial logistic-normal model. This model accounts for positive and negat

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Portela, Alberto, Julio R. Banga, and Marcos Matabuena. "Conformal prediction for uncertainty quantification in dynamic biological systems." PLOS Computational Biology 21, no. 5 (2025): e1013098. https://doi.org/10.1371/journal.pcbi.1013098.

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Uncertainty quantification (UQ) is the process of systematically determining and characterizing the degree of confidence in computational model predictions. In systems biology, and particularly with dynamic models, UQ is critical due to the nonlinearities and parameter sensitivities that influence the behavior of complex biological systems. Addressing these issues through robust UQ enables a deeper understanding of system dynamics and more reliable extrapolation beyond observed conditions. Many state-of-the-art UQ approaches in this field are grounded in Bayesian statistical methods. While the

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Ma, Junwei, Xiao Liu, Xiaoxu Niu, et al. "Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique." International Journal of Environmental Research and Public Health 17, no. 13 (2020): 4788. http://dx.doi.org/10.3390/ijerph17134788.

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Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, Q

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Lidder, Divya, Kathryn Morse, Bridget Sullivan, Wei Qian, Chenglin Miao, and Mengdi Huai. "Neuron Explanations for Conformal Prediction (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29412–14. https://doi.org/10.1609/aaai.v39i28.35270.

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Conformal prediction (CP) has gained prominence as a popular technique for uncertainty quantification in deep neural networks (DNNs), providing statistically rigorous uncertainty sets. However, existing CP methods fail to clarify the origins of predictive uncertainties. While neuron-level interpretability has been effective in revealing the internal mechanisms of DNNs, explaining CP at the neuron level remains unexplored. Nonetheless, generating neuron explanations for CP is challenging due to the discrete and non-differentiable characteristics of CP, and the labor-intensive process of semanti

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Feng, Jinchao, Joshua L. Lansford, Markos A. Katsoulakis, and Dionisios G. Vlachos. "Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences." Science Advances 6, no. 42 (2020): eabc3204. http://dx.doi.org/10.1126/sciadv.abc3204.

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Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling co

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Zgraggen, Jannik, Gianmarco Pizza, and Lilach Goren Huber. "Uncertainty Informed Anomaly Scores with Deep Learning: Robust Fault Detection with Limited Data." PHM Society European Conference 7, no. 1 (2022): 530–40. http://dx.doi.org/10.36001/phme.2022.v7i1.3342.

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Quantifying the predictive uncertainty of a model is an important ingredient in data-driven decision making. Uncertainty quantification has been gaining interest especially for deep learning models, which are often hard to justify or explain. Various techniques for deep learning based uncertainty estimates have been developed primarily for image classification and segmentation, but also for regression and forecasting tasks. Uncertainty quantification for anomaly detection tasks is still rather limited for image data and has not yet been demonstrated for machine fault detection in PHM applicati

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Kefalas, Marios, Bas van Stein, Mitra Baratchi, Asteris Apostolidis, and Thomas Baeck. "End-to-End Pipeline for Uncertainty Quantification and Remaining Useful Life Estimation: An Application on Aircraft Engines." PHM Society European Conference 7, no. 1 (2022): 245–60. http://dx.doi.org/10.36001/phme.2022.v7i1.3317.

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Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Modern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary techniques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty qua

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Banerjee, Sourav. "Uncertainty Quantification Driven Predictive Multi-Scale Model for Synthesis of Mycotoxins." Computational Biology and Bioinformatics 2, no. 1 (2014): 7. http://dx.doi.org/10.11648/j.cbb.20140201.12.

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Riley, Matthew E., and Ramana V. Grandhi. "Quantification of model-form and predictive uncertainty for multi-physics simulation." Computers & Structures 89, no. 11-12 (2011): 1206–13. http://dx.doi.org/10.1016/j.compstruc.2010.10.004.

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Olalusi, Oladimeji B., and Panagiotis Spyridis. "Probabilistic Studies on the Shear Strength of Slender Steel Fiber Reinforced Concrete Structures." Applied Sciences 10, no. 19 (2020): 6955. http://dx.doi.org/10.3390/app10196955.

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Shear failure is a brittle and undesirable mode of failure in reinforced concrete structures. Many of the existing shear design equations for steel fiber reinforced concrete (SFRC) beams include significant uncertainty due to the failure in accurately predicting the true shear capacity. Given these, adequate quantification and description of model uncertainties considering the systematic variation in the model prediction and measured shear capacity is crucial for reliability-based investigation. Reliability analysis must account for model uncertainties in order to predict the probability of fa

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Fröhlich, Alek, Thiago Ramos, Gustavo Motta Cabello Dos Santos, Isabela Panzeri Carlotti Buzatto, Rafael Izbicki, and Daniel Guimarães Tiezzi. "PersonalizedUS: Interpretable Breast Cancer Risk Assessment with Local Coverage Uncertainty Quantification." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 27998–8006. https://doi.org/10.1609/aaai.v39i27.35017.

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Correctly assessing the malignancy of breast lesions identified during ultrasound examinations is crucial for effective clinical decision-making. However, the current "gold standard" relies on manual BI-RADS scoring by clinicians, often leading to unnecessary biopsies and a significant mental health burden on patients and their families. In this paper, we introduce PersonalizedUS, an interpretable machine learning system that leverages recent advances in conformal prediction to provide precise and personalized risk estimates with local coverage guarantees and sensitivity, specificity, and pred

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Sætrom, Jon, Joakim Hove, Jan-Arild Skjervheim, and Jon Gustav Vabø. "Improved Uncertainty Quantification in the Ensemble Kalman Filter Using Statistical Model-Selection Techniques." SPE Journal 17, no. 01 (2012): 152–62. http://dx.doi.org/10.2118/145192-pa.

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Summary The ensemble Kalman filter (EnKF) is a sequential Monte Carlo method for solving nonlinear spatiotemporal inverse problems, such as petroleum-reservoir evaluation, in high dimensions. Although the EnKF has seen successful applications in numerous areas, the classical EnKF algorithm can severely underestimate the prediction uncertainty. This can lead to biased production forecasts and an ensemble collapsing into a single realization. In this paper, we combine a previously suggested EnKF scheme based on dimension reduction in the data space, with an automatic cross-validation (CV) scheme

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Cui, Xinye, Houpu Li, Yanting Yu, Shaofeng Bian, and Guojun Zhai. "A Hybrid Dropout Method for High-Precision Seafloor Topography Reconstruction and Uncertainty Quantification." Applied Sciences 15, no. 11 (2025): 6113. https://doi.org/10.3390/app15116113.

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Seafloor topography super-resolution reconstruction is critical for marine resource exploration, geological monitoring, and navigation safety. However, sparse acoustic data frequently result in the loss of high-frequency details, and traditional deep learning models exhibit limitations in uncertainty quantification, impeding their practical application. To address these challenges, this study systematically investigates the combined effects of various regularization strategies and uncertainty quantification modules. It proposes a hybrid dropout model that jointly optimizes high-precision recon

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Ding, Jing, Yizhuang David Wang, Saqib Gulzar, Youngsoo Richard Kim, and B. Shane Underwood. "Uncertainty Quantification of Simplified Viscoelastic Continuum Damage Fatigue Model using the Bayesian Inference-Based Markov Chain Monte Carlo Method." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 4 (2020): 247–60. http://dx.doi.org/10.1177/0361198120910149.

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The simplified viscoelastic continuum damage model (S-VECD) has been widely accepted as a computationally efficient and a rigorous mechanistic model to predict the fatigue resistance of asphalt concrete. It operates in a deterministic framework, but in actual practice, there are multiple sources of uncertainty such as specimen preparation errors and measurement errors which need to be probabilistically characterized. In this study, a Bayesian inference-based Markov Chain Monte Carlo method is used to quantify the uncertainty in the S-VECD model. The dynamic modulus and cyclic fatigue test data

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Dogulu, N., P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha. "Estimation of predictive hydrologic uncertainty using quantile regression and UNEEC methods and their comparison on contrasting catchments." Hydrology and Earth System Sciences Discussions 11, no. 9 (2014): 10179–233. http://dx.doi.org/10.5194/hessd-11-10179-2014.

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Abstract. In operational hydrology, estimation of predictive uncertainty of hydrological models used for flood modelling is essential for risk based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analyzing and predicting uncertainty. However, case studies comparing performance of these methods, most particularly predictive uncertainty methods, are limited. This paper focuses on two predictive uncertainty methods that differ in their methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Err

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Karimanzira, Divas. "Probabilistic Uncertainty Consideration in Regionalization and Prediction of Groundwater Nitrate Concentration." Knowledge 4, no. 4 (2024): 462–80. http://dx.doi.org/10.3390/knowledge4040025.

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In this study, we extend our previous work on a two-dimensional convolutional neural network (2DCNN) for spatial prediction of groundwater nitrate, focusing on improving uncertainty quantification. Our enhanced model incorporates a fully probabilistic Bayesian framework and a structure aimed at optimizing both specific value predictions and predictive intervals (PIs). We implemented the Prediction Interval Validation and Estimation Network based on Quality Definition (2DCNN-QD) to refine the accuracy of probabilistic predictions and reduce the width of the prediction intervals. Applied to a mo

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Heringhaus, Monika E., Yi Zhang, André Zimmermann, and Lars Mikelsons. "Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference." Sensors 22, no. 14 (2022): 5408. http://dx.doi.org/10.3390/s22145408.

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In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertaint

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Cacuci, Dan G. "TOWARDS OVERCOMING THE CURSE OF DIMENSIONALITY IN PREDICTIVE MODELLING AND UNCERTAINTY QUANTIFICATION." EPJ Web of Conferences 247 (2021): 00002. http://dx.doi.org/10.1051/epjconf/202124700002.

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This invited presentation summarizes new methodologies developed by the author for performing high-order sensitivity analysis, uncertainty quantification and predictive modeling. The presentation commences by summarizing the newly developed 3rd-Order Adjoint Sensitivity Analysis Methodology (3rd-ASAM) for linear systems, which overcomes the “curse of dimensionality” for sensitivity analysis and uncertainty quantification of a large variety of model responses of interest in reactor physics systems. The use of the exact expressions of the 2nd-, and 3rd-order sensitivities computed using the 3rd-

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Cacuci, Dan G. "TOWARDS OVERCOMING THE CURSE OF DIMENSIONALITY IN PREDICTIVE MODELLING AND UNCERTAINTY QUANTIFICATION." EPJ Web of Conferences 247 (2021): 20005. http://dx.doi.org/10.1051/epjconf/202124720005.

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This invited presentation summarizes new methodologies developed by the author for performing high-order sensitivity analysis, uncertainty quantification and predictive modeling. The presentation commences by summarizing the newly developed 3rd-Order Adjoint Sensitivity Analysis Methodology (3rd-ASAM) for linear systems, which overcomes the “curse of dimensionality” for sensitivity analysis and uncertainty quantification of a large variety of model responses of interest in reactor physics systems. The use of the exact expressions of the 2nd-, and 3rd-order sensitivities computed using the 3rd-

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Slavinskaya, N. A., M. Abbasi, J. H. Starcke, et al. "Development of an Uncertainty Quantification Predictive Chemical Reaction Model for Syngas Combustion." Energy & Fuels 31, no. 3 (2017): 2274–97. http://dx.doi.org/10.1021/acs.energyfuels.6b02319.

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Tran, Vinh Ngoc, and Jongho Kim. "Quantification of predictive uncertainty with a metamodel: toward more efficient hydrologic simulations." Stochastic Environmental Research and Risk Assessment 33, no. 7 (2019): 1453–76. http://dx.doi.org/10.1007/s00477-019-01703-0.

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Walz, Eva-Maria, Alexander Henzi, Johanna Ziegel, and Tilmann Gneiting. "Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-Valued Model Output." SIAM Review 66, no. 1 (2024): 91–122. http://dx.doi.org/10.1137/22m1541915.

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Delottier, Hugo, John Doherty, and Philip Brunner. "Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model." Geoscientific Model Development 16, no. 14 (2023): 4213–31. http://dx.doi.org/10.5194/gmd-16-4213-2023.

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Abstract. It is incumbent on decision-support hydrological modelling to make predictions of uncertain quantities in a decision-support context. In implementing decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming tasks. This is because the imposition of history-matching constraints on model parameters usually requires a large number of model runs. Data space inversion (DSI) provides a highly model-run-efficient method for predictive uncertainty quantification. It does this by evaluating covariances between model outputs us

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Incorvaia, Gabriele, Darryl Hond, and Hamid Asgari. "Uncertainty Quantification of Machine Learning Model Performance via Anomaly-Based Dataset Dissimilarity Measures." Electronics 13, no. 5 (2024): 939. http://dx.doi.org/10.3390/electronics13050939.

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The use of Machine Learning (ML) models as predictive tools has increased dramatically in recent years. However, data-driven systems (such as ML models) exhibit a degree of uncertainty in their predictions. In other words, they could produce unexpectedly erroneous predictions if the uncertainty stemming from the data, choice of model and model parameters is not taken into account. In this paper, we introduce a novel method for quantifying the uncertainty of the performance levels attained by ML classifiers. In particular, we investigate and characterize the uncertainty of model accuracy when c

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Wang, Ziqian. "Research on Stock Price Prediction Model Based on Sentiment Factor and Multi-Core Bagging Algorithm." Highlights in Business, Economics and Management 41 (October 15, 2024): 692–98. http://dx.doi.org/10.54097/hc042q45.

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Stock price prediction model is one of the key research points in the quantitative financial field. With the rapid development of data acquisition and storage technology, the data of emotional factors based on nonlinear structure is increasing. How to combine this part of information with common European technology factors to increase the ability of the model is a problem that needs to be solved. Based on this, this paper proposes a method of uncertainty quantification that combines European-and non-European data. Specifically, on the one hand, the Gaussian process regression model is used to

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Lu, Houyu, Amin Farrokhabadi, Ali Rauf, Reza Talemi, Konstantinos Gryllias, and Dimitrios Chronopoulos. "Uncertainty quantification for damage detection in 3D printed auxetic structures based on ultrasonic guided-wave using Flipout probabilistic convolutional neural network." Journal of Physics: Conference Series 2909, no. 1 (2024): 012032. https://doi.org/10.1088/1742-6596/2909/1/012032.

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Abstract The auxetic structure is widely used in aviation, bio-engineering, automation, and other industries due to its outstanding properties, such as being lightweight, high strength-to-weight ratio, and absorbing energy. Neural network methods have been popularly used for auxetic structures’ structural health monitoring. However, the performance of neural network methods in unknown areas is limited. To increase the reliability of the network model, more comprehensive uncertainty quantification is needed for damage detection in unknown areas. This paper introduces a comprehensive framework f

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Ngartera, Lebede, Mahamat Ali Issaka, and Saralees Nadarajah. "Application of Bayesian Neural Networks in Healthcare: Three Case Studies." Machine Learning and Knowledge Extraction 6, no. 4 (2024): 2639–58. http://dx.doi.org/10.3390/make6040127.

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This study aims to explore the efficacy of Bayesian Neural Networks (BNNs) in enhancing predictive modeling for healthcare applications. Advancements in artificial intelligence have significantly improved predictive modeling capabilities, with BNNs offering a probabilistic framework that addresses the inherent uncertainty and variability in healthcare data. This study demonstrates the real-world applicability of BNNs through three key case studies: personalized diabetes treatment, early Alzheimer’s disease detection, and predictive modeling for HbA1c levels. By leveraging the Bayesian approach

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