Results 41 to 50 of about 8,393,375 (379)
, 2020 The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling ...
B. Adams +17 more
semanticscholar +1 more source
On the Use of Information Theory to Quantify Parameter Uncertainty in Groundwater Modeling
Entropy, 2013 We applied information theory to quantify parameter uncertainty in a groundwater flow model. A number of parameters in groundwater modeling are often used with lack of knowledge of site conditions due to heterogeneity of hydrogeologic properties and ...
Alston Noronha, Jejung Lee
doaj +1 more source
Robust Mixed H2/H∞ Controller Design for Energy Routers in Energy Internet
Energies, 2019 In this paper, a class of mixed H2/H∞ controller is designed for an energy router (ER) within the scenario of an energy Internet (EI). The considered ER is assumed to have access with photovoltaic panels, wind turbine generators, micro-turbines, fuel ...
Haochen Hua +4 more
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SN Applied Sciences, 2022 Article highlights We review the theoretical background that supports parameter estimation and uncertainty analysis techniques applied to groundwater models.
Paulo A. Herrera +3 more
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Towards Improving the Predictive Capability of Computer Simulations by Integrating Inverse Uncertainty Quantification and Quantitative Validation with Bayesian Hypothesis Testing [PDF]
, 2021 The Best Estimate plus Uncertainty (BEPU) approach for nuclear systems modeling and simulation requires that the prediction uncertainty must be quantified in order to prove that the investigated design stays within acceptance criteria. A rigorous Uncertainty Quantification (UQ) process should simultaneously consider multiple sources of quantifiable ...
arxiv +1 more source
Bayesian estimation of copula parameters for wind speed models of dependence
IET Renewable Power Generation, 2021 Modelling the uncertainty of wind speed is essential in power flow analysis. Having abundant knowledge of the wind speed in an area is critical. A low volume of data can increase uncertainty in wind speed analysis. Spatial dependencies are often modelled
Saul B. Henderson +2 more
doaj +1 more source
Confidence Interval and Uncertainty Propagation Analysis of SAFT-type Equations of State [PDF]
arXiv, 2023 Thermodynamic models and, in particular, SAFT-type equations are vital in characterizing complex systems. This paper presents a framework for sampling parameter distributions in PC-SAFT and SAFT-VR Mie equations of state to understand parameter confidence intervals and correlations.
arxiv
Systems, 2022 We present a practical guide and step-by-step flowchart for establishing uncertainty intervals for key model outcomes in a simulation model in the face of uncertain parameters.
Wayne Wakeland, Jack Homer
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Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters [PDF]
PeerJ, 2014 Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not.
Simon van Mourik +3 more
doaj +2 more sources
Hydrology and Earth System Sciences, 2018 . Parameter uncertainty estimation is one of the major challenges in hydrological modeling. Here we present parameter uncertainty analysis of a recently released distributed conceptual hydrological model applied in the Nea catchment, Norway. Two variants
Aynom T. Teweldebrhan +2 more
semanticscholar +1 more source