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Journal articles on the topic 'Inverse Uncertainty Quantification'

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

Faes, Matthias, and David Moens. "Inverse Interval Field Quantification via Digital Image Correlation." Applied Mechanics and Materials 885 (November 2018): 304–10. http://dx.doi.org/10.4028/www.scientific.net/amm.885.304.

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This paper presents the application of a new method for the identification and quantification of interval valued spatial uncertainty under scarce data.Specifically, full-field strain measurements, obtained via Digital Image Correlation, are applied in conjunction with a quasi-static finite element model.To apply these high-dimensional but scarce data, extensions to the novel method are introduced.A case study, investigating spatial uncertainty in Young's modulus of PA-12 parts, produced via Laser Sintering, shows that an accurate quantification of the constituting uncertainty is possible, albe

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2

Hossen, MD Fayaz Bin, Tareq Alghamdi, Manal Almaeen, and Yaohang Li. "Bayesian Neural Network Variational Autoencoder Inverse Mapper (BNN-VAIM) and its application in Compton Form Factors extraction." Journal of Instrumentation 19, no. 08 (2024): C08003. http://dx.doi.org/10.1088/1748-0221/19/08/c08003.

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Abstract We extend the Variational Autoencoder Inverse Mapper (VAIM) framework for the inverse problem of extracting Compton Form Factors (CFFs) from deeply virtual exclusive reactions, such as the unpolarized Deeply virtual exclusive scattering (DVCS) cross section. VAIM is an end-to-end deep learning framework to address the solution ambiguity issue in ill-posed inverse problems, which comprises of a forward mapper and a backward mapper to simulate the forward and inverse processes, respectively. In particular, we incorporate Bayesian Neural Network (BNN) into the VAIM architecture (BNN-VAIM

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3

Wu, Tailin, Willie Neiswanger, Hongtao Zheng, Stefano Ermon, and Jure Leskovec. "Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 320–28. http://dx.doi.org/10.1609/aaai.v38i1.27785.

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Deep learning-based surrogate models have demonstrated remarkable advantages over classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over traditional partial differential equation (PDE) solvers. However, a significant challenge hindering their widespread adoption in both scientific and industrial domains is the lack of understanding about their prediction uncertainties, particularly in scenarios that involve critical decision making. To address this limitation, we propose a method that integrates efficient and precise uncertainty quantification into a deep learni

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4

Talarico, Erick Costa e. Silva, Dario Grana, Leandro Passos de Figueiredo, and Sinesio Pesco. "Uncertainty quantification in seismic facies inversion." GEOPHYSICS 85, no. 4 (2020): M43—M56. http://dx.doi.org/10.1190/geo2019-0392.1.

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In seismic reservoir characterization, facies prediction from seismic data often is formulated as an inverse problem. However, the uncertainty in the parameters that control their spatial distributions usually is not investigated. In a probabilistic setting, the vertical distribution of facies often is described by statistical models, such as Markov chains. Assuming that the transition probabilities in the vertical direction are known, the most likely facies sequence and its uncertainty can be obtained by computing the posterior distribution of a Bayesian inverse problem conditioned by seismic

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5

Khuwaileh, B. A., and H. S. Abdel-Khalik. "Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment." Nuclear Data Sheets 123 (January 2015): 57–61. http://dx.doi.org/10.1016/j.nds.2014.12.010.

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6

Tenorio, L., F. Andersson, M. de Hoop, and P. Ma. "Data analysis tools for uncertainty quantification of inverse problems." Inverse Problems 27, no. 4 (2011): 045001. http://dx.doi.org/10.1088/0266-5611/27/4/045001.

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7

Sethurajan, Athinthra, Sergey Krachkovskiy, Gillian Goward, and Bartosz Protas. "Bayesian uncertainty quantification in inverse modeling of electrochemical systems." Journal of Computational Chemistry 40, no. 5 (2018): 740–52. http://dx.doi.org/10.1002/jcc.25759.

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8

Soibam, Jerol, Ioanna Aslanidou, Konstantinos Kyprianidis, and Rebei Bel Fdhila. "Inverse flow prediction using ensemble PINNs and uncertainty quantification." International Journal of Heat and Mass Transfer 226 (July 2024): 125480. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2024.125480.

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9

Grana, Dario, Leandro Passos de Figueiredo, and Leonardo Azevedo. "Uncertainty quantification in Bayesian inverse problems with model and data dimension reduction." GEOPHYSICS 84, no. 6 (2019): M15—M24. http://dx.doi.org/10.1190/geo2019-0222.1.

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The prediction of rock properties in the subsurface from geophysical data generally requires the solution of a mathematical inverse problem. Because of the large size of geophysical (seismic) data sets and subsurface models, it is common to reduce the dimension of the problem by applying dimension reduction methods and considering a reparameterization of the model and/or the data. Especially for high-dimensional nonlinear inverse problems, in which the analytical solution of the problem is not available in a closed form and iterative sampling or optimization methods must be applied to approxim

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10

Acar, Pınar. "Uncertainty Quantification for Ti-7Al Alloy Microstructure with an Inverse Analytical Model (AUQLin)." Materials 12, no. 11 (2019): 1773. http://dx.doi.org/10.3390/ma12111773.

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The present study addresses an inverse problem for observing the microstructural stochasticity given the variations in the macro-scale material properties by developing an analytical uncertainty quantification (UQ) model called AUQLin. The uncertainty in the material property is modeled with the analytical algorithm, and then the uncertainty propagation to the microstructure is solved with an inverse problem that utilizes the transformation of random variables principle. The inverse problem leads to an underdetermined linear system, and thus produces multiple solutions to the statistical featu

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11

Hashemi, H., R. Berndtsson, M. Kompani-Zare, and M. Persson. "Natural vs. artificial groundwater recharge, quantification through inverse modeling." Hydrology and Earth System Sciences 17, no. 2 (2013): 637–50. http://dx.doi.org/10.5194/hess-17-637-2013.

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Abstract. Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady- and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncert

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12

Hashemi, H., R. Berndtsson, M. Kompani-Zare, and M. Persson. "Natural vs. artificial groundwater recharge, quantification through inverse modeling." Hydrology and Earth System Sciences Discussions 9, no. 8 (2012): 9767–807. http://dx.doi.org/10.5194/hessd-9-9767-2012.

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Abstract. Estimating the change in groundwater recharge from an introduced artificial recharge system is important in order to evaluate future water availability. This paper presents an inverse modeling approach to quantify the recharge contribution from both an ephemeral river channel and an introduced artificial recharge system based on floodwater spreading in arid Iran. The study used the MODFLOW-2000 to estimate recharge for both steady and unsteady-state conditions. The model was calibrated and verified based on the observed hydraulic head in observation wells and model precision, uncerta

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13

Liu, Mingliang, Dario Grana, and Leandro Passos de Figueiredo. "Uncertainty quantification in stochastic inversion with dimensionality reduction using variational autoencoder." GEOPHYSICS 87, no. 2 (2021): M43—M58. http://dx.doi.org/10.1190/geo2021-0138.1.

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Estimating rock and fluid properties in the subsurface from geophysical measurements is a computationally and memory-intensive inverse problem. For nonlinear problems with non-Gaussian variables, analytical solutions are generally not available, and the solutions of those inverse problems must be approximated using sampling and optimization methods. To reduce the computational cost, model and data can be reparameterized into low-dimensional spaces where the solution of the inverse problem can be computed more efficiently. Among the potential dimensionality reduction methods, deep-learning algo

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14

Zhang, Yiming, Zhiwei Pan, Shuyou Zhang, and Na Qiu. "Probabilistic invertible neural network for inverse design space exploration and reasoning." Electronic Research Archive 31, no. 2 (2022): 860–81. http://dx.doi.org/10.3934/era.2023043.

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<abstract> <p>Invertible neural network (INN) is a promising tool for inverse design optimization. While generating forward predictions from given inputs to the system response, INN enables the inverse process without much extra cost. The inverse process of INN predicts the possible input parameters for the specified system response qualitatively. For the purpose of design space exploration and reasoning for critical engineering systems, accurate predictions from the inverse process are required. Moreover, INN predictions lack effective uncertainty quantification for regression tas

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15

Domitr, Paweł, Mateusz Włostowski, Rafał Laskowski, and Romuald Jurkowski. "Comparison of inverse uncertainty quantification methods for critical flow test." Energy 263 (January 2023): 125640. http://dx.doi.org/10.1016/j.energy.2022.125640.

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16

Dashti, M., and A. M. Stuart. "Uncertainty Quantification and Weak Approximation of an Elliptic Inverse Problem." SIAM Journal on Numerical Analysis 49, no. 6 (2011): 2524–42. http://dx.doi.org/10.1137/100814664.

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17

Tian, Yuhang, Yuan Feng, and Wei Gao. "Virtual Modelling Framework-Based Inverse Study for the Mechanical Metamaterials with Material Nonlinearity." Modelling 6, no. 1 (2025): 24. https://doi.org/10.3390/modelling6010024.

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Mechanical metamaterials have become a critical research focus across various engineering fields. Recent advancements have pushed the development of reprogrammable mechanical metamaterials to achieve adaptive mechanical behaviours against external stimuli. The relevant designs strongly depend on a thorough understanding of the response spectrum of the original structure, where establishing an accurate virtual model is regarded as the most efficient approach to this end up to now. By employing an extended support vector regression (X-SVR), a powerful machine learning algorithm model, this study

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18

Yablokov, A. V., and A. S. Serdyukov. "Uncertainty quantification of phase velocity surface waves multy-modal inversion using machine learning." Interexpo GEO-Siberia 2, no. 2 (2022): 312–18. http://dx.doi.org/10.33764/2618-981x-2022-2-2-312-318.

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The paper is devoted to uncertainty quantification of the inverse problem solution of the multichannel analysis of surface waves method - the inversion of the curves of the phase velocity via frequency dependence. The uncertainty estimation approach is based on the Monte Carlo sampling strategy and a multilayer fully connected artificial neural network to approximate nonlinear dependence of shear wave velocity and layers thickness via values of phase velocity surface waves. Frequency-dependent noise in the data and errors of the inverse operator are projected onto the inverse problem solution.

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19

Bernardara, Pietro, Etienne de Rocquigny, Nicole Goutal, Aurélie Arnaud, and Giuseppe Passoni. "Uncertainty analysis in flood hazard assessment: hydrological and hydraulic calibrationThis article is one of a selection of papers published in this Special Issue on Hydrotechnical Engineering." Canadian Journal of Civil Engineering 37, no. 7 (2010): 968–79. http://dx.doi.org/10.1139/l10-056.

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Interest in the actual estimation of the uncertainty affecting flood hazard assessments is increasing within the scientific community and among decision makers. Several works may be found in the hydrological and hydraulic literature listing the sources of uncertainty affecting the estimation of extreme flood levels. Here, a well-assessed uncertainty treatment procedure is applied to carry out a complete flood hazard assessment study to encompass both the hydrological and hydraulic components. In particular, the focus is on modeling the sources of uncertainty via a direct (for discharge) or inv

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20

Malinverno, Alberto, and Victoria A. Briggs. "Expanded uncertainty quantification in inverse problems: Hierarchical Bayes and empirical Bayes." GEOPHYSICS 69, no. 4 (2004): 1005–16. http://dx.doi.org/10.1190/1.1778243.

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A common way to account for uncertainty in inverse problems is to apply Bayes' rule and obtain a posterior distribution of the quantities of interest given a set of measurements. A conventional Bayesian treatment, however, requires assuming specific values for parameters of the prior distribution and of the distribution of the measurement errors (e.g., the standard deviation of the errors). In practice, these parameters are often poorly known a priori, and choosing a particular value is often problematic. Moreover, the posterior uncertainty is computed assuming that these parameters are fixed;

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21

Bardsley, Johnathan M., and Colin Fox. "An MCMC method for uncertainty quantification in nonnegativity constrained inverse problems." Inverse Problems in Science and Engineering 20, no. 4 (2012): 477–98. http://dx.doi.org/10.1080/17415977.2011.637208.

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22

Liu, Junhong, Shanfang Huang, Xiaoyu Guo, Jiageng Wang, and Kan Wang. "INVERSE UNCERTAINTY QUANTIFICATION OF CTF PHYSICAL MODEL PARAMETERS USING BAYESIAN INFERENCE." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2019.27 (2019): 1435. http://dx.doi.org/10.1299/jsmeicone.2019.27.1435.

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23

Lu, Yen-An, Wei-Shou Hu, Joel A. Paulson, and Qi Zhang. "BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantification." Computers & Chemical Engineering 192 (January 2025): 108859. http://dx.doi.org/10.1016/j.compchemeng.2024.108859.

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24

Nagel, Joseph B., and Bruno Sudret. "A unified framework for multilevel uncertainty quantification in Bayesian inverse problems." Probabilistic Engineering Mechanics 43 (January 2016): 68–84. http://dx.doi.org/10.1016/j.probengmech.2015.09.007.

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25

Klein, Olaf, Daniele Davino, and Ciro Visone. "On forward and inverse uncertainty quantification for models involving hysteresis operators." Mathematical Modelling of Natural Phenomena 15 (2020): 53. http://dx.doi.org/10.1051/mmnp/2020009.

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Parameters within hysteresis operators modeling real world objects have to be identified from measurements and are therefore subject to corresponding errors. To investigate the influence of these errors, the methods of Uncertainty Quantification (UQ) are applied. Results of forward UQ for a play operator with a stochastic yield limit are presented. Moreover, inverse UQ is performed to identify the parameters in the weight function in a Prandtl-Ishlinskiĭ operator and the uncertainties of these parameters.

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26

Yang, Xiu, Weixuan Li, and Alexandre Tartakovsky. "Sliced-Inverse-Regression--Aided Rotated Compressive Sensing Method for Uncertainty Quantification." SIAM/ASA Journal on Uncertainty Quantification 6, no. 4 (2018): 1532–54. http://dx.doi.org/10.1137/17m1148955.

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27

Repetti, Audrey, Marcelo Pereyra, and Yves Wiaux. "Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization." SIAM Journal on Imaging Sciences 12, no. 1 (2019): 87–118. http://dx.doi.org/10.1137/18m1173629.

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28

Giordano, Matteo, and Hanne Kekkonen. "Bernstein--von Mises Theorems and Uncertainty Quantification for Linear Inverse Problems." SIAM/ASA Journal on Uncertainty Quantification 8, no. 1 (2020): 342–73. http://dx.doi.org/10.1137/18m1226269.

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29

Fang, Zhilong, Curt Da Silva, Rachel Kuske, and Felix J. Herrmann. "Uncertainty quantification for inverse problems with weak partial-differential-equation constraints." GEOPHYSICS 83, no. 6 (2018): R629—R647. http://dx.doi.org/10.1190/geo2017-0824.1.

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In statistical inverse problems, the objective is a complete statistical description of unknown parameters from noisy observations to quantify uncertainties in unknown parameters. We consider inverse problems with partial-differential-equation (PDE) constraints, which are applicable to many seismic problems. Bayesian inference is one of the most widely used approaches to precisely quantify statistics through a posterior distribution, incorporating uncertainties in observed data, modeling kernel, and prior knowledge of parameters. Typically when formulating the posterior distribution, the PDE c

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30

Wang, Chen, Xu Wu, Ziyu Xie, and Tomasz Kozlowski. "Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference." Energies 16, no. 22 (2023): 7664. http://dx.doi.org/10.3390/en16227664.

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Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an important tool for quantifying the uncertainties in the physical model parameters (PMPs) while making the model predictions consistent with the experimental data. In this paper, we present an extension to an existing Bayesian inference-based IUQ methodology by employing a hierarchical Bayesian model and variational inference (VI), and apply this novel framework to a real-world nuclear TH scenario. The proposed approach l

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31

Most, Thomas. "Inverse Uncertainty Quantification in Material Parameter Calibration Using Probabilistic and Interval Approaches." Applied Mechanics 6, no. 1 (2025): 14. https://doi.org/10.3390/applmech6010014.

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In model calibration, the identification of the unknown parameter values themselves, but also the uncertainty of these model parameters, due to uncertain measurements or model outputs might be required. The analysis of parameter uncertainty helps us understand the calibration problem better. Investigations on the parameter sensitivity and the uniqueness of the identified parameters could be addressed within uncertainty quantification. In this paper, we investigate different probabilistic approaches for this purpose, which identify the unknown parameters as multivariate distribution functions.

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32

Super, Ingrid, Stijn N. C. Dellaert, Antoon J. H. Visschedijk, and Hugo A. C. Denier van der Gon. "Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design." Atmospheric Chemistry and Physics 20, no. 3 (2020): 1795–816. http://dx.doi.org/10.5194/acp-20-1795-2020.

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Abstract. Quantification of greenhouse gas emissions is receiving a lot of attention because of its relevance for climate mitigation. Complementary to official reported bottom-up emission inventories, quantification can be done with an inverse modelling framework, combining atmospheric transport models, prior gridded emission inventories and a network of atmospheric observations to optimize the emission inventories. An important aspect of such a method is a correct quantification of the uncertainties in all aspects of the modelling framework. The uncertainties in gridded emission inventories a

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33

Honjo, Yusuke, and Thuraisamy Thavaraj. "On uncertainty evaluation of contaminant migration through clayey barriers." Canadian Geotechnical Journal 31, no. 5 (1994): 637–48. http://dx.doi.org/10.1139/t94-076.

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This paper presents a methodology to estimate parameters and to make predictions with quantified uncertainty for an advective–diffusive transport of nonreactive species and low-concentration reactive species through saturated porous media. The methodology is put in the framework of inverse and forward analyses. The maximum-likelihood method (or the weighted least square method) is employed in the inverse analysis, whereas the first-order second-moment method is used in the forward analysis. The methodology facilitates the quantification of uncertainty in the estimated parameters as well as in

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34

Zhou, Qing, Xin’an Wang, and Feng Mao. "Numerical analysis of a water-LBE interaction experiment: Sensitivity analysis, inverse uncertainty quantification and uncertainty propagation." Nuclear Engineering and Design 438 (July 2025): 114035. https://doi.org/10.1016/j.nucengdes.2025.114035.

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35

Berg, Steffen, Evren Unsal, and Harm Dijk. "Non-uniqueness and uncertainty quantification of relative permeability measurements by inverse modelling." Computers and Geotechnics 132 (April 2021): 103964. http://dx.doi.org/10.1016/j.compgeo.2020.103964.

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36

de Vries, Kevin, Anna Nikishova, Benjamin Czaja, Gábor Závodszky, and Alfons G. Hoekstra. "INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL." International Journal for Uncertainty Quantification 10, no. 4 (2020): 333–49. http://dx.doi.org/10.1615/int.j.uncertaintyquantification.2020033186.

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37

Faes, Matthias, Matteo Broggi, Edoardo Patelli, et al. "A multivariate interval approach for inverse uncertainty quantification with limited experimental data." Mechanical Systems and Signal Processing 118 (March 2019): 534–48. http://dx.doi.org/10.1016/j.ymssp.2018.08.050.

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38

Hu, Guojun, and Tomasz Kozlowski. "Inverse uncertainty quantification of trace physical model parameters using BFBT benchmark data." Annals of Nuclear Energy 96 (October 2016): 197–203. http://dx.doi.org/10.1016/j.anucene.2016.05.021.

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39

Wu, Xu, Koroush Shirvan, and Tomasz Kozlowski. "Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification." Journal of Computational Physics 396 (November 2019): 12–30. http://dx.doi.org/10.1016/j.jcp.2019.06.032.

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40

Lan, Shiwei, Shuyi Li, and Babak Shahbaba. "Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks." SIAM/ASA Journal on Uncertainty Quantification 10, no. 4 (2022): 1684–713. http://dx.doi.org/10.1137/21m1439456.

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41

Dixon, J. R., B. A. Lindley, T. Taylor, and G. T. Parks. "DATA ASSIMILATION APPLIED TO PRESSURISED WATER REACTORS." EPJ Web of Conferences 247 (2021): 09020. http://dx.doi.org/10.1051/epjconf/202124709020.

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Best estimate plus uncertainty is the leading methodology to validate existing safety margins. It remains a challenge to develop and license these approaches, in part due to the high dimensionality of system codes. Uncertainty quantification is an active area of research to develop appropriate methods for propagating uncertainties, offering greater scientific reason, dimensionality reduction and minimising reliance on expert judgement. Inverse uncertainty quantification is required to infer a best estimate back on the input parameters and reduce the uncertainties, but it is challenging to capt

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42

Ma, Xiaopeng, Kai Zhang, Liming Zhang, et al. "Data-Driven Niching Differential Evolution with Adaptive Parameters Control for History Matching and Uncertainty Quantification." SPE Journal 26, no. 02 (2021): 993–1010. http://dx.doi.org/10.2118/205014-pa.

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Summary History matching is a typical inverse problem that adjusts the uncertainty parameters of the reservoir numerical model with limited dynamic response data. In most situations, various parameter combinations can result in the same data fit, termed as nonuniqueness of inversion. It is desirable to find as many global or local optima as possible in a single optimization run, which may help to reveal the distribution of the uncertainty parameters in the posterior space, which is particularly important for robust optimization, risk analysis, and decision making in reservoir management. Howev

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Abdollahzadeh, Asaad, Alan Reynolds, Mike Christie, David Corne, Brian Davies, and Glyn Williams. "Bayesian Optimization Algorithm Applied to Uncertainty Quantification." SPE Journal 17, no. 03 (2012): 865–73. http://dx.doi.org/10.2118/143290-pa.

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Summary Prudent decision making in subsurface assets requires reservoir uncertainty quantification. In a typical uncertainty-quantification study, reservoir models must be updated using the observed response from the reservoir by a process known as history matching. This involves solving an inverse problem, finding reservoir models that produce, under simulation, a similar response to that of the real reservoir. However, this requires multiple expensive multiphase-flow simulations. Thus, uncertainty-quantification studies employ optimization techniques to find acceptable models to be used in p

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44

Lartaud, Paul, Philippe Humbert, and Josselin Garnier. "Uncertainty quantification in Bayesian inverse problems with neutron and gamma time correlation measurements." Annals of Nuclear Energy 213 (April 2025): 111123. https://doi.org/10.1016/j.anucene.2024.111123.

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45

Banks, H. T., Kathleen Holm, and Danielle Robbins. "Standard error computations for uncertainty quantification in inverse problems: Asymptotic theory vs. bootstrapping." Mathematical and Computer Modelling 52, no. 9-10 (2010): 1610–25. http://dx.doi.org/10.1016/j.mcm.2010.06.026.

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46

Li, Weixuan, Guang Lin, and Bing Li. "Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice." Journal of Computational Physics 321 (September 2016): 259–78. http://dx.doi.org/10.1016/j.jcp.2016.05.040.

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47

Zhang, Chi, Martin F. Lambert, Jinzhe Gong, Aaron C. Zecchin, Angus R. Simpson, and Mark L. Stephens. "Bayesian Inverse Transient Analysis for Pipeline Condition Assessment: Parameter Estimation and Uncertainty Quantification." Water Resources Management 34, no. 9 (2020): 2807–20. http://dx.doi.org/10.1007/s11269-020-02582-9.

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48

Polydorides, N. "A stochastic simulation method for uncertainty quantification in the linearized inverse conductivity problem." International Journal for Numerical Methods in Engineering 90, no. 1 (2011): 22–39. http://dx.doi.org/10.1002/nme.3305.

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49

Tinfena, G., M. Angelucci, L. Sargentini, S. Paci, and L. E. Herranz. "Inverse uncertainty Quantification in the Severe accident Domain: Application to Fission Product release." Nuclear Engineering and Design 436 (May 2025): 113954. https://doi.org/10.1016/j.nucengdes.2025.113954.

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50

Fan, Ming, Zezhong Zhang, Dan Lu, and Guannan Zhang. "GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI." SoftwareX 31 (September 2025): 102232. https://doi.org/10.1016/j.softx.2025.102232.

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