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Artykuły w czasopismach na temat "Uncertainty Quantification model"
Salehghaffari, S., i 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, nr 10 (8.01.2013): 2165–81. http://dx.doi.org/10.1177/0954406212473390.
Pełny tekst źródłaVallam, P., X. S. Qin i 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.
Pełny tekst źródłaGuo, Xianpeng, Dezhi Wang, Lilun Zhang, Yongxian Wang, Wenbin Xiao i Xinghua Cheng. "Uncertainty Quantification of Underwater Sound Propagation Loss Integrated with Kriging Surrogate Model". International Journal of Signal Processing Systems 5, nr 4 (grudzień 2017): 141–45. http://dx.doi.org/10.18178/ijsps.5.4.141-145.
Pełny tekst źródłaFranck, Isabell M., i P. S. Koutsourelakis. "Constitutive model error and uncertainty quantification". PAMM 17, nr 1 (grudzień 2017): 865–68. http://dx.doi.org/10.1002/pamm.201710400.
Pełny tekst źródłade Vries, Douwe K., i Paul M. J. Den Van Hof. "Quantification of model uncertainty from data". International Journal of Robust and Nonlinear Control 4, nr 2 (1994): 301–19. http://dx.doi.org/10.1002/rnc.4590040206.
Pełny tekst źródłaKamga, P. H. T., B. Li, M. McKerns, L. H. Nguyen, M. Ortiz, H. Owhadi i T. J. Sullivan. "Optimal uncertainty quantification with model uncertainty and legacy data". Journal of the Mechanics and Physics of Solids 72 (grudzień 2014): 1–19. http://dx.doi.org/10.1016/j.jmps.2014.07.007.
Pełny tekst źródłaLiu, Chang, i Duane A. McVay. "Continuous Reservoir-Simulation-Model Updating and Forecasting Improves Uncertainty Quantification". SPE Reservoir Evaluation & Engineering 13, nr 04 (12.08.2010): 626–37. http://dx.doi.org/10.2118/119197-pa.
Pełny tekst źródłaCheng, Xi, Clément Henry, Francesco P. Andriulli, Christian Person i 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, nr 7 (9.04.2020): 2586. http://dx.doi.org/10.3390/ijerph17072586.
Pełny tekst źródłaSun, Xianming, i Michèle Vanmaele. "Uncertainty Quantification of Derivative Instruments". East Asian Journal on Applied Mathematics 7, nr 2 (maj 2017): 343–62. http://dx.doi.org/10.4208/eajam.100316.270117a.
Pełny tekst źródłaHerty, Michael, i Elisa Iacomini. "Uncertainty quantification in hierarchical vehicular flow models". Kinetic and Related Models 15, nr 2 (2022): 239. http://dx.doi.org/10.3934/krm.2022006.
Pełny tekst źródłaRozprawy doktorskie na temat "Uncertainty Quantification model"
Fadikar, Arindam. "Stochastic Computer Model Calibration and Uncertainty Quantification". Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/91985.
Pełny tekst źródłaDoctor 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.
Pełny tekst źródłaPark, 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.
Pełny tekst źródłaBlumer, Joel David. "Cross-scale model validation with aleatory and epistemic uncertainty". Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53571.
Pełny tekst źródłaEzvan, Olivier. "Multilevel model reduction for uncertainty quantification in computational structural dynamics". Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1109/document.
Pełny tekst źródłaThis 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.
Pełny tekst źródłaRiley, 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.
Pełny tekst źródłaRashidi, Mehrabadi Niloofar. "Power Electronics Design Methodologies with Parametric and Model-Form Uncertainty Quantification". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82934.
Pełny tekst źródłaPh. D.
Xie, Yimeng. "Advancements in Degradation Modeling, Uncertainty Quantification and Spatial Variable Selection". Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/71687.
Pełny tekst źródłaPh. 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.
Pełny tekst źródłaKsiążki na temat "Uncertainty Quantification model"
Mao, Zhu, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-77348-9.
Pełny tekst źródłaMao, Zhu, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-04090-0.
Pełny tekst źródłaBarthorpe, Robert, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-74793-4.
Pełny tekst źródłaAtamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou i Tyler Schoenherr, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04552-8.
Pełny tekst źródłaBarthorpe, Robert, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-12075-7.
Pełny tekst źródłaAtamturktur, Sez, Tyler Schoenherr, Babak Moaveni i Costas Papadimitriou, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5.
Pełny tekst źródłaBarthorpe, Robert, Roland Platz, Israel Lopez, Babak Moaveni i Costas Papadimitriou, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54858-6.
Pełny tekst źródłaMao, Zhu, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47638-0.
Pełny tekst źródłaAtamturktur, H. Sezer, Babak Moaveni, Costas Papadimitriou i Tyler Schoenherr, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0.
Pełny tekst źródłaPlatz, Roland, Garrison Flynn, Kyle Neal i Scott Ouellette, red. Model Validation and Uncertainty Quantification, Volume 3. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-37003-8.
Pełny tekst źródłaCzęści książek na temat "Uncertainty Quantification model"
Sun, Ne-Zheng, i Alexander Sun. "Model Uncertainty Quantification". W 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.
Pełny tekst źródłaNouy, Anthony. "Low-Rank Tensor Methods for Model Order Reduction". W Handbook of Uncertainty Quantification, 857–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_21.
Pełny tekst źródłaChen, Peng, i Christoph Schwab. "Model Order Reduction Methods in Computational Uncertainty Quantification". W Handbook of Uncertainty Quantification, 937–90. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_70.
Pełny tekst źródłaNouy, Anthony. "Low-Rank Tensor Methods for Model Order Reduction". W Handbook of Uncertainty Quantification, 1–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_21-1.
Pełny tekst źródłaChen, Peng, i Christoph Schwab. "Model Order Reduction Methods in Computational Uncertainty Quantification". W Handbook of Uncertainty Quantification, 1–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_70-1.
Pełny tekst źródłaSuárez-Taboada, María, Jeroen A. S. Witteveen, Lech A. Grzelak i Cornelis W. Oosterlee. "Uncertainty Quantification and Heston Model". W 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.
Pełny tekst źródłaJiang, Zhen, Paul D. Arendt, Daniel W. Apley i Wei Chen. "Multi-response Approach to Improving Identifiability in Model Calibration". W Handbook of Uncertainty Quantification, 69–127. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_65.
Pełny tekst źródłaJiang, Zhen, Paul D. Arendt, Daniel W. Apley i Wei Chen. "Multi-response Approach to Improving Identifiability in Model Calibration". W Handbook of Uncertainty Quantification, 1–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_65-1.
Pełny tekst źródłaBijak, Jakub, i Jason Hilton. "Uncertainty Quantification, Model Calibration and Sensitivity". W Towards Bayesian Model-Based Demography, 71–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83039-7_5.
Pełny tekst źródłaGattiker, James, Kary Myers, Brian J. Williams, Dave Higdon, Marcos Carzolio i Andrew Hoegh. "Gaussian Process-Based Sensitivity Analysis and Bayesian Model Calibration with GPMSA". W Handbook of Uncertainty Quantification, 1867–907. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_58.
Pełny tekst źródłaStreszczenia konferencji na temat "Uncertainty Quantification model"
Andrews, Stephen A., i Brandon M. Wilson. "Variational Bayesian Calibration of a PTW Material Strength Model for OFHC Copper". W ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/vvuq2023-108829.
Pełny tekst źródłaEshraghi, Shaun, Michael Carolan, Benjamin Perlman i Francisco González III. "Finite Element Model Validation of Cryogenic DOT-113 Tank Car Side Impact Tests". W ASME 2024 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/vvuq2024-132617.
Pełny tekst źródłaKirsch, Jared, William Rider i Nima Fathi. "Credibility Assessment of Machine Learning-Based Surrogate Model Predictions on NACA 0012 Airfoil Flow". W ASME 2024 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/vvuq2024-132964.
Pełny tekst źródłaTartaruga, Irene, Jonathan E. Cooper, Georgia Georgiou i Hamed Khodaparast. "Flutter Uncertainty Quantification for the S4T Model". W 55th AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1653.
Pełny tekst źródłaAarts, Arne, Wil Michiels i Peter Roelse. "Leveraging Partial Model Extractions using Uncertainty Quantification". W 2021 IEEE 10th International Conference on Cloud Networking (CloudNet). IEEE, 2021. http://dx.doi.org/10.1109/cloudnet53349.2021.9657130.
Pełny tekst źródłaDavis, Brad, Gregory Langone i Nicholas Reisweber. "Sensitivity Analysis and Bayesian Calibration of a Holmquist-Johnson-Cook Material Model for Cellular Concrete Subjected to Impact Loading". W ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86800.
Pełny tekst źródłaJiang, Zhen, Wei Li, Daniel W. Apley i Wei Chen. "A System Uncertainty Propagation Approach With Model Uncertainty Quantification in Multidisciplinary Design". W 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.
Pełny tekst źródłaGiagopoulos, Dimitrios, Alexandros Arailopoulos, Ilias Zacharakis i Eleni Pipili. "FINITE ELEMENT MODEL DEVELOPED AND MODAL ANALYSIS OF LARGE SCALE STEAM TURBINE ROTOR: QUANTIFICATION OF UNCERTAINTIES AND MODEL UPDATING". W 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.
Pełny tekst źródłaLaboulfie, Clément, Matthieu Balesdent, Loïc Brevault, Sébastien Da Veiga, François-Xavier Irisarri, Rodolphe Le Riche i Jean-François Maire. "CALIBRATION OF MATERIAL MODEL PARAMETERS USING MIXED-EFFECTS MODEL". W 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.
Pełny tekst źródłaEl Garroussi, Siham, Matthias De Lozzo, Sophie Ricci, Didier Lucor, Nicole Goutal, Cédric Goeury i Sébastien Boyaval. "UNCERTAINTY QUANTIFICATION IN A TWO-DIMENSIONAL RIVER HYDRAULIC MODEL". W 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.
Pełny tekst źródłaRaporty organizacyjne na temat "Uncertainty Quantification model"
Gonzales, Lindsey M., Thomas M. Hall, Kendra L. Van Buren, Steven R. Anton i Francois M. Hemez. Quantification of Prediction Bounds Caused by Model Form Uncertainty. Office of Scientific and Technical Information (OSTI), wrzesień 2013. http://dx.doi.org/10.2172/1095195.
Pełny tekst źródłaLawrence, Earl Christopher, i Brian Phillip Weaver. Model Emulation and Calibration: Uncertainty Quantification and Making Inference with Simulation. Office of Scientific and Technical Information (OSTI), maj 2019. http://dx.doi.org/10.2172/1514917.
Pełny tekst źródłaWeirs, V. Gregory. Dakota uncertainty quantification methods applied to the NEK-5000 SAHEX model. Office of Scientific and Technical Information (OSTI), marzec 2014. http://dx.doi.org/10.2172/1155019.
Pełny tekst źródłaLogan, R., C. Nitta i S. Chidester. Estimating Parametric, Model Form, and Solution Contributions Using Integral Validation Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), luty 2006. http://dx.doi.org/10.2172/894762.
Pełny tekst źródłaHund, Lauren, i Justin Brown. Statistically Rigorous Uncertainty Quantification for Physical Parameter Model Calibration with Functional Output. Office of Scientific and Technical Information (OSTI), wrzesień 2016. http://dx.doi.org/10.2172/1562417.
Pełny tekst źródłaTezaur, Irina Kalashnikova, Maciej Balajewicz, Matthew F. Barone, Kevin Thomas Carlberg, Jeffrey A. Fike i Erin E. Mussoni. Model Reduction for Compressible Cavity Simulations Towards Uncertainty Quantification of Structural Loading. Office of Scientific and Technical Information (OSTI), wrzesień 2016. http://dx.doi.org/10.2172/1562432.
Pełny tekst źródłaMaulik, Romit, Virendra Ghate, William Pringle, Yan Feng, Vishwas Rao, Julie Bessac i Bethany Lusch. Surrogate multi-fidelity data and model fusion forscientific discovery and uncertainty quantification inEarth System Models. Office of Scientific and Technical Information (OSTI), kwiecień 2021. http://dx.doi.org/10.2172/1769781.
Pełny tekst źródłaAcquesta, Erin, Teresa Portone, Raj Dandekar, Chris Rackauckas, Rileigh Bandy, Jose Huerta i India Dytzel. Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology. Office of Scientific and Technical Information (OSTI), wrzesień 2022. http://dx.doi.org/10.2172/1888443.
Pełny tekst źródłaWang, Dali, Shih-Chieh Kao i 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), kwiecień 2021. http://dx.doi.org/10.2172/1769696.
Pełny tekst źródłaChung, Bub Dong, Young Lee Lee, Chan Eok Park i 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), październik 1996. http://dx.doi.org/10.2172/393372.
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