Letteratura scientifica selezionata sul tema "Uncertainty Quantification model"

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 suffer from long burn-in times and many required runs for chain stabilization. In this paper, we apply the MCMC in a real-time reservoirmodeling application. The system operates in a continuous process of data acquisition, model calibration, forecasting, and uncertainty quantification. The system was validated on the PUNQ (production forecasting with uncertainty quantification) synthetic reservoir in a simulated multiyear continuous-modeling scenario, and it yielded probabilistic forecasts that narrowed with time. Once the continuous MCMC simulation process has been established sufficiently, the continuous approach usually allows generation of a reasonable probabilistic forecast at a particular point in time with many fewer models than the traditional application of the MCMC method in a one-time, static simulation study starting at the same time. Operating continuously over the many years of typical reservoir life, many more realizations can be run than with traditional approaches. This allows more-thorough investigation of the parameter space and more-complete quantification of forecast uncertainty. More importantly, the approach provides a mechanism for, and can thus encourage, calibration of uncertainty estimates over time. Greater investigation of the uncertain parameter space and calibration of uncertainty estimates by using a continuous modeling process should improve the reliability of probabilistic forecasts significantly.

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.

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 model, with each model parameter specified in an interval.

<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 differential equations on the hydrodynamic level are given. The effect of the possibly negative diffusion in the stochastic hydrodynamic model is studied and numerical simulations of uncertain traffic situations are presented.</p>