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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Active learning-assisted directed evolution by Jason Yang, Ravi G. Lal, James C. Bowden, Raul Astudillo, Mikhail A. Hameedi, Sukhvinder Kaur, Matthew Hill, Yisong Yue, Frances H. Arnold

“…Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods. …”
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AI-Assisted Forecasting of a Mitigated Multiple Steam Generator Tube Rupture Scenario in a Typical Nuclear Power Plant by Sonia Spisak, Aya Diab

“…Following this, uncertainty quantification employing the Best Estimate Plus Uncertainty (BEPU) methodology was undertaken by coupling RELAP5/SCDAPSIM/MOD3.4 with the statistical software, DAKOTA 6.14.0. …”
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PSI Methodologies for Nuclear Data Uncertainty Propagation with CASMO-5M and MCNPX: Results for OECD/NEA UAM Benchmark Phase I by W. Wieselquist, T. Zhu, A. Vasiliev, H. Ferroukhi

“…Capabilities for uncertainty quantification (UQ) with respect to nuclear data have been developed at PSI in the recent years and applied to the UAM benchmark. …”
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Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation by O. Lamminpää, J. Susiluoto, J. Hobbs, J. McDuffie, A. Braverman, H. Owhadi

“…<p>Remote sensing of atmospheric carbon dioxide (CO<span class="inline-formula">₂</span>) carried out by NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite mission and the related uncertainty quantification effort involve repeated evaluations of a state-of-the-art atmospheric physics model. …”
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A comparative analysis of fractal and fractionalized thermal non-equilibrium model for chaotic convection saturated by porous medium by Kashif Ali Abro, Imran Qasim Memon, Ali Yousef, Qasem M. Al-Mdallal

“…The sensitivity analysis for chaos expansion and uncertainty quantification for the flow in heterogeneous media have been perceived to the problem of chaotic convection through numerical simulations. …”
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Uncertainty impact of isotherm models on liquid-phase adsorption thermodynamics: A bayesian inference perspective by Thiago Reschützegger, Nina Paula Gonçalves Salau

“…This study advocates for a balanced approach to model choice, incorporating uncertainty quantification to enhance the reliability of adsorption predictions in both research and industrial applications.…”
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Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran by Javad Zahiri, Mohammad Reza Nikoo, Adell Moradi-Sabzkouhi, Mitra Cheraghi, Nazmi Mat Nawi

“…By leveraging advanced regression techniques and uncertainty quantification, the findings enable more reliable predictions of TDS levels, which are crucial for sustainable river management.…”
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Advances in the pilot point inverse method: Où En Sommes-Nous maintenant? by White, Jeremy, Lavenue, Marsh

“…A technique that was once confined to academic realms, the PPM is now widely accepted as one of the industry pillars of inversion and uncertainty quantification for predictive groundwater modeling. …”
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A compilation of surface inherent optical properties and phytoplankton pigment concentrations from the Atlantic Meridional Transect by T. M. Jordan, G. Dall'Olmo, G. Tilstone, R. J. W. Brewin, F. Nencioli, R. Airs, C. S. Thomas, L. Schlüter

“…The IOP data are processed using a consistent methodology and include propagated uncertainties for each IOP variable, uncertainty quantification for the Tot_Chl_a concentrations based on HPLC match-ups, application of consistent quality-control filters, and standardization of output data fields and formats. …”
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