Academic literature on the topic 'Uncertainty Quantification model'
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Journal articles on the topic "Uncertainty Quantification model"
Salehghaffari, S., and M. Rais-Rohani. "Material model uncertainty quantification using evidence theory." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 10 (January 8, 2013): 2165–81. http://dx.doi.org/10.1177/0954406212473390.
Full textVallam, P., X. S. Qin, and J. J. Yu. "Uncertainty Quantification of Hydrologic Model." APCBEE Procedia 10 (2014): 219–23. http://dx.doi.org/10.1016/j.apcbee.2014.10.042.
Full textGuo, Xianpeng, Dezhi Wang, Lilun Zhang, Yongxian Wang, Wenbin Xiao, and Xinghua Cheng. "Uncertainty Quantification of Underwater Sound Propagation Loss Integrated with Kriging Surrogate Model." International Journal of Signal Processing Systems 5, no. 4 (December 2017): 141–45. http://dx.doi.org/10.18178/ijsps.5.4.141-145.
Full textFranck, Isabell M., and P. S. Koutsourelakis. "Constitutive model error and uncertainty quantification." PAMM 17, no. 1 (December 2017): 865–68. http://dx.doi.org/10.1002/pamm.201710400.
Full textde Vries, Douwe K., and Paul M. J. Den Van Hof. "Quantification of model uncertainty from data." International Journal of Robust and Nonlinear Control 4, no. 2 (1994): 301–19. http://dx.doi.org/10.1002/rnc.4590040206.
Full textKamga, P. H. T., B. Li, M. McKerns, L. H. Nguyen, M. Ortiz, H. Owhadi, and T. J. Sullivan. "Optimal uncertainty quantification with model uncertainty and legacy data." Journal of the Mechanics and Physics of Solids 72 (December 2014): 1–19. http://dx.doi.org/10.1016/j.jmps.2014.07.007.
Full textLiu, Chang, and Duane A. McVay. "Continuous Reservoir-Simulation-Model Updating and Forecasting Improves Uncertainty Quantification." SPE Reservoir Evaluation & Engineering 13, no. 04 (August 12, 2010): 626–37. http://dx.doi.org/10.2118/119197-pa.
Full textCheng, Xi, Clément Henry, Francesco P. Andriulli, Christian Person, and Joe Wiart. "A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data." International Journal of Environmental Research and Public Health 17, no. 7 (April 9, 2020): 2586. http://dx.doi.org/10.3390/ijerph17072586.
Full textSun, Xianming, and Michèle Vanmaele. "Uncertainty Quantification of Derivative Instruments." East Asian Journal on Applied Mathematics 7, no. 2 (May 2017): 343–62. http://dx.doi.org/10.4208/eajam.100316.270117a.
Full textHerty, Michael, and Elisa Iacomini. "Uncertainty quantification in hierarchical vehicular flow models." Kinetic and Related Models 15, no. 2 (2022): 239. http://dx.doi.org/10.3934/krm.2022006.
Full textDissertations / Theses on the topic "Uncertainty Quantification model"
Fadikar, Arindam. "Stochastic Computer Model Calibration and Uncertainty Quantification." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91985.
Full textDoctor of Philosophy
Mathematical models are versatile and often provide accurate description of physical events. Scientific models are used to study such events in order to gain understanding of the true underlying system. These models are often complex in nature and requires advance algorithms to solve their governing equations. Outputs from these models depend on external information (also called model input) supplied by the user. Model inputs may or may not have a physical meaning, and can sometimes be only specific to the scientific model. More often than not, optimal values of these inputs are unknown and need to be estimated from few actual observations. This process is known as inverse problem, i.e. inferring the input from the output. The inverse problem becomes challenging when the mathematical model is stochastic in nature, i.e., multiple execution of the model result in different outcome. In this dissertation, three methodologies are proposed that talk about the calibration and prediction of a stochastic disease simulation model which simulates contagion of an infectious disease through human-human contact. The motivating examples are taken from the Ebola epidemic in West Africa in 2014 and seasonal flu in New York City in USA.
White, Jeremy. "Computer Model Inversion and Uncertainty Quantification in the Geosciences." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5329.
Full textPark, Inseok. "Quantification of Multiple Types of Uncertainty in Physics-Based Simulation." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1348702461.
Full textBlumer, Joel David. "Cross-scale model validation with aleatory and epistemic uncertainty." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53571.
Full textEzvan, Olivier. "Multilevel model reduction for uncertainty quantification in computational structural dynamics." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1109/document.
Full textThis work deals with an extension of the classical construction of reduced-order models (ROMs) that are obtained through modal analysis in computational linear structural dynamics. It is based on a multilevel projection strategy and devoted to complex structures with uncertainties. Nowadays, it is well recognized that the predictions in structural dynamics over a broad frequency band by using a finite element model must be improved in taking into account the model uncertainties induced by the modeling errors, for which the role increases with the frequency. In such a framework, the nonparametric probabilistic approach of uncertainties is used, which requires the introduction of a ROM. Consequently, these two aspects, frequency-evolution of the uncertainties and reduced-order modeling, lead us to consider the development of a multilevel ROM in computational structural dynamics, which has the capability to adapt the level of uncertainties to each part of the frequency band. In this thesis, we are interested in the dynamical analysis of complex structures in a broad frequency band. By complex structure is intended a structure with complex geometry, constituted of heterogeneous materials and more specifically, characterized by the presence of several structural levels, for instance, a structure that is made up of a stiff main part embedding various flexible sub-parts. For such structures, it is possible having, in addition to the usual global-displacements elastic modes associated with the stiff skeleton, the apparition of numerous local elastic modes, which correspond to predominant vibrations of the flexible sub-parts. For such complex structures, the modal density may substantially increase as soon as low frequencies, leading to high-dimension ROMs with the modal analysis method (with potentially thousands of elastic modes in low frequencies). In addition, such ROMs may suffer from a lack of robustness with respect to uncertainty, because of the presence of the numerous local displacements, which are known to be very sensitive to uncertainties. It should be noted that in contrast to the usual long-wavelength global displacements of the low-frequency (LF) band, the local displacements associated with the structural sub-levels, which can then also appear in the LF band, are characterized by short wavelengths, similarly to high-frequency (HF) displacements. As a result, for the complex structures considered, there is an overlap of the three vibration regimes, LF, MF, and HF, and numerous local elastic modes are intertwined with the usual global elastic modes. This implies two major difficulties, pertaining to uncertainty quantification and to computational efficiency. The objective of this thesis is thus double. First, to provide a multilevel stochastic ROM that is able to take into account the heterogeneous variability introduced by the overlap of the three vibration regimes. Second, to provide a predictive ROM whose dimension is decreased with respect to the classical ROM of the modal analysis method. A general method is presented for the construction of a multilevel ROM, based on three orthogonal reduced-order bases (ROBs) whose displacements are either LF-, MF-, or HF-type displacements (associated with the overlapping LF, MF, and HF vibration regimes). The construction of these ROBs relies on a filtering strategy that is based on the introduction of global shape functions for the kinetic energy (in contrast to the local shape functions of the finite elements). Implementing the nonparametric probabilistic approach in the multilevel ROM allows each type of displacements to be affected by a particular level of uncertainties. The method is applied to a car, for which the multilevel stochastic ROM is identified with respect to experiments, solving a statistical inverse problem. The proposed ROM allows for obtaining a decreased dimension as well as an improved prediction with respect to a classical stochastic ROM
Chiang, Shen. "Hydrological model comparison and refinement through uncertainty recognition and quantification." 京都大学 (Kyoto University), 2005. http://hdl.handle.net/2433/144539.
Full textRiley, Matthew E. "Quantification of Model-Form, Predictive, and Parametric Uncertainties in Simulation-Based Design." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1314895435.
Full textRashidi, Mehrabadi Niloofar. "Power Electronics Design Methodologies with Parametric and Model-Form Uncertainty Quantification." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82934.
Full textPh. D.
Xie, Yimeng. "Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/71687.
Full textPh. D.
Karlén, Johan. "Uncertainty Quantification of a Large 1-D Dynamic Aircraft System Simulation Model." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-120189.
Full textBooks on the topic "Uncertainty Quantification model"
Mao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-77348-9.
Full textMao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-04090-0.
Full textBarthorpe, Robert, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74793-4.
Full textAtamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou, and Tyler Schoenherr, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04552-8.
Full textBarthorpe, Robert, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-12075-7.
Full textAtamturktur, Sez, Tyler Schoenherr, Babak Moaveni, and Costas Papadimitriou, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5.
Full textBarthorpe, Robert, Roland Platz, Israel Lopez, Babak Moaveni, and Costas Papadimitriou, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54858-6.
Full textMao, Zhu, ed. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0.
Full textAtamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou, and Tyler Schoenherr, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0.
Full textPlatz, Roland, Garrison Flynn, Kyle Neal, and Scott Ouellette, eds. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-37003-8.
Full textBook chapters on the topic "Uncertainty Quantification model"
Sun, Ne-Zheng, and Alexander Sun. "Model Uncertainty Quantification." In Model Calibration and Parameter Estimation, 407–58. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6_10.
Full textNouy, Anthony. "Low-Rank Tensor Methods for Model Order Reduction." In Handbook of Uncertainty Quantification, 857–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_21.
Full textChen, Peng, and Christoph Schwab. "Model Order Reduction Methods in Computational Uncertainty Quantification." In Handbook of Uncertainty Quantification, 937–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_70.
Full textNouy, Anthony. "Low-Rank Tensor Methods for Model Order Reduction." In Handbook of Uncertainty Quantification, 1–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_21-1.
Full textChen, Peng, and Christoph Schwab. "Model Order Reduction Methods in Computational Uncertainty Quantification." In Handbook of Uncertainty Quantification, 1–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_70-1.
Full textSuárez-Taboada, María, Jeroen A. S. Witteveen, Lech A. Grzelak, and Cornelis W. Oosterlee. "Uncertainty Quantification and Heston Model." In Progress in Industrial Mathematics at ECMI 2016, 153–59. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63082-3_22.
Full textJiang, Zhen, Paul D. Arendt, Daniel W. Apley, and Wei Chen. "Multi-response Approach to Improving Identifiability in Model Calibration." In Handbook of Uncertainty Quantification, 69–127. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_65.
Full textJiang, Zhen, Paul D. Arendt, Daniel W. Apley, and Wei Chen. "Multi-response Approach to Improving Identifiability in Model Calibration." In Handbook of Uncertainty Quantification, 1–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_65-1.
Full textBijak, Jakub, and Jason Hilton. "Uncertainty Quantification, Model Calibration and Sensitivity." In Towards Bayesian Model-Based Demography, 71–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83039-7_5.
Full textGattiker, James, Kary Myers, Brian J. Williams, Dave Higdon, Marcos Carzolio, and Andrew Hoegh. "Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA." In Handbook of Uncertainty Quantification, 1867–907. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_58.
Full textConference papers on the topic "Uncertainty Quantification model"
Andrews, Stephen A., and Brandon M. Wilson. "Variational Bayesian Calibration of a PTW Material Strength Model for OFHC Copper." In ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/vvuq2023-108829.
Full textEshraghi, Shaun, Michael Carolan, Benjamin Perlman, and Francisco González III. "Finite Element Model Validation of Cryogenic DOT-113 Tank Car Side Impact Tests." In ASME 2024 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/vvuq2024-132617.
Full textKirsch, Jared, William Rider, and Nima Fathi. "Credibility Assessment of Machine Learning-Based Surrogate Model Predictions on NACA 0012 Airfoil Flow." In ASME 2024 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/vvuq2024-132964.
Full textTartaruga, Irene, Jonathan E. Cooper, Georgia Georgiou, and Hamed Khodaparast. "Flutter Uncertainty Quantification for the S4T Model." In 55th AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1653.
Full textAarts, Arne, Wil Michiels, and Peter Roelse. "Leveraging Partial Model Extractions using Uncertainty Quantification." In 2021 IEEE 10th International Conference on Cloud Networking (CloudNet). IEEE, 2021. http://dx.doi.org/10.1109/cloudnet53349.2021.9657130.
Full textDavis, Brad, Gregory Langone, and Nicholas Reisweber. "Sensitivity Analysis and Bayesian Calibration of a Holmquist-Johnson-Cook Material Model for Cellular Concrete Subjected to Impact Loading." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86800.
Full textJiang, Zhen, Wei Li, Daniel W. Apley, and Wei Chen. "A System Uncertainty Propagation Approach With Model Uncertainty Quantification in Multidisciplinary Design." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34708.
Full textGiagopoulos, Dimitrios, Alexandros Arailopoulos, Ilias Zacharakis, and Eleni Pipili. "FINITE ELEMENT MODEL DEVELOPED AND MODAL ANALYSIS OF LARGE SCALE STEAM TURBINE ROTOR: QUANTIFICATION OF UNCERTAINTIES AND MODEL UPDATING." In 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2017. http://dx.doi.org/10.7712/120217.5349.16898.
Full textLaboulfie, Clément, Matthieu Balesdent, Loïc Brevault, Sébastien Da Veiga, François-Xavier Irisarri, Rodolphe Le Riche, and Jean-François Maire. "CALIBRATION OF MATERIAL MODEL PARAMETERS USING MIXED-EFFECTS MODEL." In 4th International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Research and Development for Computational Methods in Engineering Sciences (ICMES), 2021. http://dx.doi.org/10.7712/120221.8037.18933.
Full textEl Garroussi, Siham, Matthias De Lozzo, Sophie Ricci, Didier Lucor, Nicole Goutal, Cédric Goeury, and Sébastien Boyaval. "UNCERTAINTY QUANTIFICATION IN A TWO-DIMENSIONAL RIVER HYDRAULIC MODEL." In 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2019. http://dx.doi.org/10.7712/120219.6339.18380.
Full textReports on the topic "Uncertainty Quantification model"
Gonzales, Lindsey M., Thomas M. Hall, Kendra L. Van Buren, Steven R. Anton, and Francois M. Hemez. Quantification of Prediction Bounds Caused by Model Form Uncertainty. Office of Scientific and Technical Information (OSTI), September 2013. http://dx.doi.org/10.2172/1095195.
Full textLawrence, Earl Christopher, and Brian Phillip Weaver. Model Emulation and Calibration: Uncertainty Quantification and Making Inference with Simulation. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1514917.
Full textWeirs, V. Gregory. Dakota uncertainty quantification methods applied to the NEK-5000 SAHEX model. Office of Scientific and Technical Information (OSTI), March 2014. http://dx.doi.org/10.2172/1155019.
Full textLogan, R., C. Nitta, and S. Chidester. Estimating Parametric, Model Form, and Solution Contributions Using Integral Validation Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), February 2006. http://dx.doi.org/10.2172/894762.
Full textHund, Lauren, and Justin Brown. Statistically Rigorous Uncertainty Quantification for Physical Parameter Model Calibration with Functional Output. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562417.
Full textTezaur, Irina Kalashnikova, Maciej Balajewicz, Matthew F. Barone, Kevin Thomas Carlberg, Jeffrey A. Fike, and Erin E. Mussoni. Model Reduction for Compressible Cavity Simulations Towards Uncertainty Quantification of Structural Loading. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562432.
Full textMaulik, Romit, Virendra Ghate, William Pringle, Yan Feng, Vishwas Rao, Julie Bessac, and Bethany Lusch. Surrogate multi-fidelity data and model fusion forscientific discovery and uncertainty quantification inEarth System Models. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769781.
Full textAcquesta, Erin, Teresa Portone, Raj Dandekar, Chris Rackauckas, Rileigh Bandy, Jose Huerta, and India Dytzel. Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1888443.
Full textWang, Dali, Shih-Chieh Kao, and Daniel Ricciuto. Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769696.
Full textChung, Bub Dong, Young Lee Lee, Chan Eok Park, and Sang Yong Lee. Improvements to the RELAP5/MOD3 reflood model and uncertainty quantification of reflood peak clad temperature. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/393372.
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