A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins by Thi-Duyen Nguyen, Duc Hai Nguyen, Hyun-Han Kwon, Deg-Hyo Bae

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A probabilistic model-based approach to assess and minimize scaling in geothermal plants by Pejman Shoeibi Omrani, Jonah Poort, Eduardo G. D. Barros, Hidde de Zwart, Cintia Gonçalves Machado, Laura Wasch, Aris Twerda, Huub H. M. Rijnaarts, Shahab Shariat Torbaghan

Subjects: “…Uncertainty quantification…”
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Fitting Geometric Shapes to Fuzzy Point Cloud Data by Vincent B. Verhoeven, Pasi Raumonen, Markku Åkerblom

Subjects: “…uncertainty quantification…”
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Uncertainty quantification of rock mass discontinuity information and mechanical parameter derived from photogrammetry and Monte Carlo simulation: A case study by Xiaoyu Yi, Wenkai Feng, Yongjian Zhou, Wenxuan Wu, Jiachen Zhao

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Finite strain formulation of the discrete equilibrium gap principle: application to direct parameter estimation from large full-fields measurements by Peyraut, Alice, Genet, Martin

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Quantifying the survival uncertainty of Wolbachia-infected mosquitoes in a spatial model by Martin Strugarek, Nicolas Vauchelet, Jorge P. Zubelli

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Cloudy with a chance of uncertainty: autoconversion rates forecasting via evidential regression from satellite data – CORRIGENDUM by Maria Carolina Novitasari, Johannes Quaas, Miguel R. D. Rodrigues

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On the Measurement of Laser Lines in 3D Space with Uncertainty Estimation by Ivan De Boi, Nasser Ghaderi, Steve Vanlanduit, Rudi Penne

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Multi-level uncertain fatigue analysis of a truss under incomplete available information by R. B. P. Nonato

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Data-driven upper bounds and event attribution for unprecedented heatwaves by Mark D. Risser, Likun Zhang, Michael F. Wehner

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A Martingale-Free Introduction to Conditional Gaussian Nonlinear Systems by Marios Andreou, Nan Chen

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Best-Estimate Plus Uncertainty Framework for Multiscale, Multiphysics Light Water Reactor Core Analysis by Jason Hou, Maria Avramova, Kostadin Ivanov

“…Similarly, the uncertainty quantification on thermal-hydraulic models is established on a relatively small scale, while these results will be used in Phase III at the core scale, sometimes with different codes or models. …”
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Aggregating multiple test results to improve medical decision-making. by Lucas Böttcher, Maria R D'Orsogna, Tom Chou

“…We also provide the corresponding uncertainty quantification.…”
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Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. by Matthew Leming, Sudeshna Das, Hyungsoon Im

“…By combining MUCRAN and the uncertainty quantification method, we showed consistent and significant increases in the AD detection accuracy for newly collected MGH data (post-2019; 84.6% with MUCRAN vs. 72.5% without MUCRAN) and for data from other hospitals (90.3% from Brigham and Women's Hospital and 81.0% from other hospitals). …”
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Smolyak-Grid-Based Flutter Analysis with the Stochastic Aerodynamic Uncertainty by Yuting Dai, Chao Yang

“…Afterwards, the methodology for flutter uncertainty quantification due to aerodynamic perturbation was developed, based on the nonintrusive polynomial chaos expansion theory. …”
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Time-Dependent Reliability-Based Design Optimization Utilizing Nonintrusive Polynomial Chaos by Yao Wang, Shengkui Zeng, Jianbin Guo

“…Polynomial chaos combined with the moving least squares (PCMLS) is presented as a nonintrusive time-dependent surrogate model to conduct uncertainty quantification. Wear is considered to be a critical failure that deteriorates the kinematic reliability and the structural reliability through the changing kinematics. …”
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Exploring parameter dependence of atomic minima with implicit differentiation by Ivan Maliyov, Petr Grigorev, Thomas D. Swinburne

“…Forward propagation of parameter variation is key for uncertainty quantification, whilst backpropagation has found application for emerging inverse problems such as fine-tuning or targeted design. …”
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A software tool for applying Bayes' theorem in medical diagnostics by Theodora Chatzimichail, Aristides T. Hatjimihail

“…It provides a framework for their uncertainty quantification and assists in understanding and applying Bayes' theorem in medical diagnostics.…”
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Use of Response Surface Metamodels for Identification of Stiffness and Damping Coefficients in a Simple Dynamic System by A.C. Rutherford, D.J. Inman, G. Park, F.M. Hemez

“…In structural dynamics applications, response surface metamodels have been utilized in a forward sense, for activities such as sensitivity analysis or uncertainty quantification. In this study a polynomial response surface model is developed, relating system parameters to measurable output features. …”
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Modeling Groundwater Resources in Data-Scarce Regions for Sustainable Management: Methodologies and Limits by Iolanda Borzì

“…Future research should focus on improving the integration of diverse data sources, enhancing the representation of complex hydrogeological processes in simplified models, and developing robust uncertainty quantification methods tailored for data-scarce conditions.…”
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