Thematische Bibliographien / Inverse Uncertainty Quantification

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Inverse Uncertainty Quantification“

Autor: Grafiati
Veröffentlicht am 25. Juni 2021
Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Inverse Uncertainty Quantification"

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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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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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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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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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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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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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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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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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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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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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Dissertationen zum Thema "Inverse Uncertainty Quantification"

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Hebbur, Venkata Subba Rao Vishwas. "Adjoint based solution and uncertainty quantification techniques for variational inverse problems." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76665.

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Variational inverse problems integrate computational simulations of physical phenomena with physical measurements in an informational feedback control system. Control parameters of the computational model are optimized such that the simulation results fit the physical measurements.The solution procedure is computationally expensive since it involves running the simulation computer model (the emph{forward model}) and the associated emph {adjoint model} multiple times. In practice, our knowledge of the underlying physics is incomplete and hence the associated computer model is laden with emph {
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Devathi, Duttaabhinivesh. "Uncertainty Quantification for Underdetermined Inverse Problems via Krylov Subspace Iterative Solvers." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case155446130705089.

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Chue, Bryan C. "Efficient Hessian computation in inverse problems with application to uncertainty quantification." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21138.

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Thesis (M.Sc.Eng.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>This thesis considers the efficient Hessian computation in inverse problems with specific application to the elastography inverse problem. Inverse problems use measurements of observable parameters to infer information about m
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Andersson, Hjalmar. "Inverse Uncertainty Quantification using deterministic sampling : An intercomparison between different IUQ methods." Thesis, Uppsala universitet, Tillämpad kärnfysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447070.

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In this thesis, two novel methods for Inverse Uncertainty Quantification are benchmarked against the more established methods of Monte Carlo sampling of output parameters(MC) and Maximum Likelihood Estimation (MLE). Inverse Uncertainty Quantification (IUQ) is the process of how to best estimate the values of the input parameters in a simulation, and the uncertainty of said estimation, given a measurement of the output parameters. The two new methods are Deterministic Sampling (DS) and Weight Fixing (WF). Deterministic sampling uses a set of sampled points such that the set of points has the sa
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Lal, Rajnesh. "Data assimilation and uncertainty quantification in cardiovascular biomechanics." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS088/document.

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Les simulations numériques des écoulements sanguins cardiovasculaires peuvent combler d’importantes lacunes dans les capacités actuelles de traitement clinique. En effet, elles offrent des moyens non invasifs pour quantifier l’hémodynamique dans le cœur et les principaux vaisseaux sanguins chez les patients atteints de maladies cardiovasculaires. Ainsi, elles permettent de recouvrer les caractéristiques des écoulements sanguins qui ne peuvent pas être obtenues directement à partir de l’imagerie médicale. Dans ce sens, des simulations personnalisées utilisant des informations propres aux patien
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Narayanamurthi, Mahesh. "Advanced Time Integration Methods with Applications to Simulation, Inverse Problems, and Uncertainty Quantification." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104357.

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Simulation and optimization of complex physical systems are an integral part of modern science and engineering. The systems of interest in many fields have a multiphysics nature, with complex interactions between physical, chemical and in some cases even biological processes. This dissertation seeks to advance forward and adjoint numerical time integration methodologies for the simulation and optimization of semi-discretized multiphysics partial differential equations (PDEs), and to estimate and control numerical errors via a goal-oriented a posteriori error framework. We extend exponential
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Ray, Kolyan Michael. "Asymptotic theory for Bayesian nonparametric procedures in inverse problems." Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/278387.

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The main goal of this thesis is to investigate the frequentist asymptotic properties of nonparametric Bayesian procedures in inverse problems and the Gaussian white noise model. In the first part, we study the frequentist posterior contraction rate of nonparametric Bayesian procedures in linear inverse problems in both the mildly and severely ill-posed cases. This rate provides a quantitative measure of the quality of statistical estimation of the procedure. A theorem is proved in a general Hilbert space setting under approximation-theoretic assumptions on the prior. The result is applied to n
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Alhossen, Iman. "Méthode d'analyse de sensibilité et propagation inverse d'incertitude appliquées sur les modèles mathématiques dans les applications d'ingénierie." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30314/document.

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Dans de nombreuses disciplines, les approches permettant d'étudier et de quantifier l'influence de données incertaines sont devenues une nécessité. Bien que la propagation directe d'incertitudes ait été largement étudiée, la propagation inverse d'incertitudes demeure un vaste sujet d'étude, sans méthode standardisée. Dans cette thèse, une nouvelle méthode de propagation inverse d'incertitude est présentée. Le but de cette méthode est de déterminer l'incertitude d'entrée à partir de données de sortie considérées comme incertaines. Parallèlement, les méthodes d'analyse de sensibilité sont égalem
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Gehre, Matthias [Verfasser], Peter [Akademischer Betreuer] Maaß, and Bangti [Akademischer Betreuer] Jin. "Rapid Uncertainty Quantification for Nonlinear Inverse Problems / Matthias Gehre. Gutachter: Peter Maaß ; Bangti Jin. Betreuer: Peter Maaß." Bremen : Staats- und Universitätsbibliothek Bremen, 2013. http://d-nb.info/1072078589/34.

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Kamilis, Dimitrios. "Uncertainty Quantification for low-frequency Maxwell equations with stochastic conductivity models." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31415.

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Uncertainty Quantification (UQ) has been an active area of research in recent years with a wide range of applications in data and imaging sciences. In many problems, the source of uncertainty stems from an unknown parameter in the model. In physical and engineering systems for example, the parameters of the partial differential equation (PDE) that model the observed data may be unknown or incompletely specified. In such cases, one may use a probabilistic description based on prior information and formulate a forward UQ problem of characterising the uncertainty in the PDE solution and observati
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Bücher zum Thema "Inverse Uncertainty Quantification"

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Bardsley, Johnathan M. Computational Uncertainty Quantification for Inverse Problems. Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975383.

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Bardsley, Johnathan M. Computational Uncertainty Quantification for Inverse Problems. Society for Industrial and Applied Mathematics, 2018.

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Biegler, Lorenz, George Biros, Omar Ghattas, Matthias Heinkenschloss, and Bani Mallick. Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley & Sons, Incorporated, John, 2011.

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Biegler, Lorenz, George Biros, Omar Ghattas, et al., eds. Large‐Scale Inverse Problems and Quantification of Uncertainty. Wiley, 2010. http://dx.doi.org/10.1002/9780470685853.

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Biegler, Lorenz, George Biros, Omar Ghattas, Matthias Heinkenschloss, and Bani Mallick. Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley & Sons, Incorporated, John, 2011.

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Large-scale inverse problems and quantification of uncertainty. Wiley, 2010.

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Keyes, David, Lorenz Biegler, George Biros, Omar Ghattas, and Matthias Heinkenschloss. Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley & Sons, Incorporated, John, 2010.

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Biegler, Lorenz, George Biros, Omar Ghattas, Matthias Heinkenschloss, and Bani Mallick. Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley & Sons, Incorporated, John, 2010.

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Biegler, Lorenz, George Biros, Omar Ghattas, Matthias Heinkenschloss, and Bani Mallick. Large-Scale Inverse Problems and Quantification of Uncertainty. Wiley & Sons, Limited, John, 2010.

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Tenorio, Luis. Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems. Society for Industrial and Applied Mathematics, 2017.

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Buchteile zum Thema "Inverse Uncertainty Quantification"

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Soize, Christian. "Fundamental Tools for Statistical Inverse Problems." In Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54339-0_7.

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Dashti, Masoumeh, and Andrew M. Stuart. "The Bayesian Approach to Inverse Problems." In Handbook of Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_7.

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Dashti, Masoumeh, and Andrew M. Stuart. "The Bayesian Approach to Inverse Problems." In Handbook of Uncertainty Quantification. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_7-1.

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Soize, Christian. "Random Vectors and Random Fields in High Dimension: Parametric Model-Based Representation, Identification from Data, and Inverse Problems." In Handbook of Uncertainty Quantification. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_30.

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Soize, Christian. "Random Vectors and Random Fields in High Dimension: Parametric Model-Based Representation, Identification from Data, and Inverse Problems." In Handbook of Uncertainty Quantification. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_30-1.

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Kitanidis, P. K. "Bayesian and Geostatistical Approaches to Inverse Problems." In Large-Scale Inverse Problems and Quantification of Uncertainty. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470685853.ch4.

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Sandu, A. "Solution of Inverse Problems using Discrete ODE Adjoints." In Large-Scale Inverse Problems and Quantification of Uncertainty. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470685853.ch16.

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Qiao, Baijie, Zhu Mao, Jinxin Liu, and Xuefeng Chen. "Sparse Deconvolution for the Inverse Problem of Multiple-Impact Force Identification." In Model Validation and Uncertainty Quantification, Volume 3. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74793-4_1.

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Delbos, F., C. Duffet, and D. Sinoquet. "Uncertainty Analysis for Seismic Inverse Problems: Two Practical Examples." In Large-Scale Inverse Problems and Quantification of Uncertainty. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470685853.ch15.

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Zabaras, N. "Solving Stochastic Inverse Problems: A Sparse Grid Collocation Approach." In Large-Scale Inverse Problems and Quantification of Uncertainty. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470685853.ch14.

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Konferenzberichte zum Thema "Inverse Uncertainty Quantification"

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Huang, Luzhe, Jianing Li, Xiaofu Ding, Yijie Zhang, Hanlong Chen, and Aydogan Ozcan. "Neural network uncertainty quantification in inverse imaging problems using cycle consistency." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.af1b.3.

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We present an uncertainty quantification method for neural networks solving inverse imaging problems. We leverage the physical forward model to establish forward-backward cycles to quantify inference uncertainty and detect out-of-distribution data in computational imaging tasks.
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Wang, Bao, Xiaoyu Pang, WeiWei Men, Zhun Wei, and Siyuan He. "Effect of Equivariance in Uncertainty Quantification for Full-Wave Inverse Scattering." In 2024 IEEE 10th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE). IEEE, 2024. https://doi.org/10.1109/mape62875.2024.10813891.

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Xie, Ziyu, Wen Jiang, Congjian Wang, and Xu Wu. "Inverse Uncertainty Quantification of a MOOSE-based Melt Pool Model for Additive Manufacturing." In Mathematics and Computation 2021. American Nuclear Society, 2021. https://doi.org/10.13182/xyz-33939.

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Xie, Ziyu, and Xu Wu. "A Comprehensive Framework to Improve Predictions by Integrating Inverse Uncertainty Quantification and Quantitative Validation." In Mathematics and Computation 2021. American Nuclear Society, 2021. https://doi.org/10.13182/xyz-33820.

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Kim, Haeseong, Sacit Cetiner, and Matteo Bucci. "Inverse Problem Approach for Estimating Operating Conditions and Uncertainty Quantification in Actual Forced Convection System." In 2024 International Congress on Advances in Nuclear Power Plants (ICAPP). American Nuclear Society, 2024. http://dx.doi.org/10.13182/t130-44242.

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Pandit, Priyanka, Arjun Earthperson, and Mihai Diaconeasa. "Leveraging Inverse Uncertainty Quantification to Enhance the Resilience of Nuclear Power Plant Construction Duration Estimation." In Advanced Reactor Safety (ARS). American Nuclear Society, 2024. http://dx.doi.org/10.13182/t130-43602.

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Lartaud, Paul, Philippe Humbert, and Josselin Garnier. "Bayesian Inverse Problem and Uncertainty Quantification in the Joint Analysis of Neutron and Gamma Corrrelations." In International Conference on Physics of Reactors (PHYSOR 2024). American Nuclear Society, 2024. http://dx.doi.org/10.13182/physor24-43485.

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Wang, Liwei, Shuangshuang Jin, and Zheyu Zhang. "Sample-Reduced Uncertainty Quantification Method on PV Inverter Reliability Assessment Using Unscented Transformation." In 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC). IEEE, 2024. http://dx.doi.org/10.1109/pvsc57443.2024.10749678.

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Friswell, Michael, Jose Fonseca, John Mottershead, and Arthur Lees. "Quantification of Uncertainty Using Inverse Methods." In 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-1672.

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Houpert, Corentin, Josselin Garnier, and Philippe Humbert. "INVERSE PROBLEMS FOR STOCHASTIC NEUTRONICS." In 4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Institute of Research and Development for Computational Methods in Engineering Sciences (ICMES), 2021. http://dx.doi.org/10.7712/120221.8022.18997.

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Berichte der Organisationen zum Thema "Inverse Uncertainty Quantification"

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Horne, Steven. Uncertainty Quantification in GADRAS Inverse Modeling. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2462991.

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Fowler, Michael James. Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1179471.

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Favorite, Jeffrey A., Garrett James Dean, Keith C. Bledsoe, et al. Predictive Modeling, Inverse Problems, and Uncertainty Quantification with Application to Emergency Response. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1432629.

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Romero, Vicente, Clay Sanders, Timothy Walsh, and Cameron McCormick. Pragmatic Uncertainty Quantification and Propagation in Inverse Estimation of Structural Dynamics Parameters given Material Property Uncertainties and Limited Sensor Data. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2432203.

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Biros, George. Uncertainity Quantification for Large Scale Inverse Scattering. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada578547.

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