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Letteratura scientifica selezionata sul tema "Uncertainty Quantification model"

Autore: Grafiati

Pubblicato: 31 agosto 2024

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Articoli di riviste sul tema "Uncertainty Quantification model"

1

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 (2013): 2165–81. http://dx.doi.org/10.1177/0954406212473390.

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Uncertainties in material models and their influence on structural behavior and reliability are important considerations in analysis and design of structures. In this article, a methodology based on the evidence theory is presented for uncertainty quantification of constitutive models. The proposed methodology is applied to Johnson–Cook plasticity model while considering various sources of uncertainty emanating from experimental stress–strain data as well as method of fitting the model constants and representation of the nondimensional temperature. All uncertain parameters are represented in i

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2

Vallam, 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.

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3

Guo, 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 (2017): 141–45. http://dx.doi.org/10.18178/ijsps.5.4.141-145.

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4

Franck, Isabell M., and P. S. Koutsourelakis. "Constitutive model error and uncertainty quantification." PAMM 17, no. 1 (2017): 865–68. http://dx.doi.org/10.1002/pamm.201710400.

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5

de 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.

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6

Kamga, P. H. T., B. Li, M. McKerns, et al. "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.

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7

Liu, Chang, and Duane A. McVay. "Continuous Reservoir-Simulation-Model Updating and Forecasting Improves Uncertainty Quantification." SPE Reservoir Evaluation & Engineering 13, no. 04 (2010): 626–37. http://dx.doi.org/10.2118/119197-pa.

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Abstract (sommario):

Summary Most reservoir-simulation studies are conducted in a static context—at a single point in time using a fixed set of historical data for history matching. Time and budget constraints usually result in significant reduction in the number of uncertain parameters and incomplete exploration of the parameter space, which results in underestimation of forecast uncertainty and less-than-optimal decision making. Markov Chain Monte Carlo (MCMC) methods have been used in static studies for rigorous exploration of the parameter space for quantification of forecast uncertainty, but these methods suf

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8

Cheng, 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 (2020): 2586. http://dx.doi.org/10.3390/ijerph17072586.

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This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.

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9

Sun, Xianming, and Michèle Vanmaele. "Uncertainty Quantification of Derivative Instruments." East Asian Journal on Applied Mathematics 7, no. 2 (2017): 343–62. http://dx.doi.org/10.4208/eajam.100316.270117a.

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AbstractModel and parameter uncertainties are common whenever some parametric model is selected to value a derivative instrument. Combining the Monte Carlo method with the Smolyak interpolation algorithm, we propose an accurate efficient numerical procedure to quantify the uncertainty embedded in complex derivatives. Except for the value function being sufficiently smooth with respect to the model parameters, there are no requirements on the payoff or candidate models. Numerical tests carried out quantify the uncertainty of Bermudan put options and down-and-out put options under the Heston mod

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10

Herty, 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.

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Abstract (sommario):

<p style='text-indent:20px;'>We consider kinetic vehicular traffic flow models of BGK type [<xref ref-type="bibr" rid="b24">24</xref>]. Considering different spatial and temporal scales, those models allow to derive a hierarchy of traffic models including a hydrodynamic description. In this paper, the kinetic BGK–model is extended by introducing a parametric stochastic variable to describe possible uncertainty in traffic. The interplay of uncertainty with the given model hierarchy is studied in detail. Theoretical results on consistent formulations of the stochastic different

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