Bayesian sensitivity analysis of a model of the aortic valve

Becker, W.; Rowson, Jennifer; Oakley, Jeremy E.; Yoxall, Alaster; Manson, G.; Worden, Keith

doi:10.1016/j.jbiomech.2011.03.008

Cited by 39 publications

(25 citation statements)

References 18 publications

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“…Therefore, f (x) could be considered an unknown function, since the output is unknown for a given set of inputs until the model has actually been run. If however, the function (model) is sampled at a number of carefully chosen input points, it is possible to t a response surface (footnote: A response surface is the hypersurface of the model output as a result of varying the inputs [13].) which can predict the output of the model for any point in the input space without having to run the model.…”

Section: A Gaussian Processesmentioning

confidence: 99%

“…To be successful the emulator must be general and as little as possible must be assumed about the emulator function. The emulator should also be able to accurately imitate the model using as few training points as possible [13].…”

Section: A Gaussian Processesmentioning

confidence: 99%

“…A particular probabilistic approach for developing an emulator is the use of Gaussian Process rather than a single number or a xed-length vector [13].…”

Section: A Gaussian Processesmentioning

confidence: 99%

“…This is a reasonable assumption since many engineering models display roughly linear behavior with respect to at least some of the model inputs [13]. Here the covariance between output points is dened by a squared-exponential function of the form [7]:…”

Section: A Gaussian Processesmentioning

confidence: 99%

“…The hyperparameters, w, σ 2 , B, are the controlling parameters that dene the behavior of the emulator, which allows the emulator to be general enough for a wide range of engineering problems [13]. The squaredexponential covariance function is by no means the only covariance function -many others are detailed in Rasmussen and Williams [17].…”

Section: A Gaussian Processesmentioning

confidence: 99%

See 4 more Smart Citations

Bayesian Sensitivity Analysis of Flight Parameters in a Hard-Landing Analysis Process

Sartor

Becker

Worden

et al. 2016

Journal of Aircraft

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Section: A Gaussian Processesmentioning

confidence: 99%

Section: A Gaussian Processesmentioning

confidence: 99%

“…A particular probabilistic approach for developing an emulator is the use of Gaussian Process rather than a single number or a xed-length vector [13].…”

Section: A Gaussian Processesmentioning

confidence: 99%

Section: A Gaussian Processesmentioning

confidence: 99%

Section: A Gaussian Processesmentioning

confidence: 99%

See 3 more Smart Citations

Bayesian Sensitivity Analysis of Flight Parameters in a Hard-Landing Analysis Process

Sartor

Becker

Worden

et al. 2016

Journal of Aircraft

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The Water Retention Index: Using land use planning to manage water resources in Europe

Vandecasteele

Rivero

Claudia

et al. 2018

Sustainable Development

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Appropriate land management can be an effective approach to improving water quantity regulation. There is, however, a need to identify both where measures are most needed and where they may be most effective. The water retention index (WRI) was developed with this goal in mind.The WRI is a composite indicator which takes into account parameters reflecting potential water retention in vegetation, water bodies, soil and underlying aquifers, as well as the influence of slope and artificially sealed areas. Three land management scenarios were simulated up to 2030 using the LUISA modeling platform: increasing grassland in upstream areas as well as afforestation in both upstream areas and riparian zones. The WRI was computed for all scenarios as well as a comparative "business-as-usual" baseline scenario. All scenarios showed an overall improvement of the index as compared to this baseline, with afforestation in upstream areas having the greatest effect. The WRI can provide useful insights into the current capacity of a landscape to regulate water as well as the effectiveness of possible remediation strategies applied at the European scale. The management of both the quality and the quantity of Europe's water resources remains a major concern, especially given the current and potential future rate of urbanization. This increased population pressure and higher degree of soil sealing can lead to a reduction in both condition and quantity of natural areas. This paper looks more specifically at the potential impacts on water quantity management over time.An indicator is presented which reflects the landscape's capacity to regulate water (i.e., intercept, retain and store the water flowing through it), and which can be used to assess the potential impacts of land use changes over time. De Groot, Wilson, and Boumans (2002) state that water regulation is the role of land cover in regulating runoff and river discharge. Several approaches have been taken to quantify water regulation, including using infiltration rate (Le Clec'h et al., 2016) and/or soil water storage capacity (Laterra, Orúe, & Booman, 2012; Smith, De Groot, & Bergkamp, 2006;TEEB, 2010) ologies, however, are data-intensive and/or require specific models to be used. Some studies have already looked at more comprehensive, less data-intensive ways of estimating water retention or regulation capacity, both for smaller study areas (Šatalová & Kenderessy, 2017) and at --------------------------------------------------------------------------------------------------------------------------------This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Estimation of kinematic parameters in a model of artificial aortic valve leaflets

et al. 2017

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Bayesian sensitivity analysis of a model of the aortic valve

Cited by 39 publications

References 18 publications

Bayesian Sensitivity Analysis of Flight Parameters in a Hard-Landing Analysis Process

Bayesian Sensitivity Analysis of Flight Parameters in a Hard-Landing Analysis Process

The Water Retention Index: Using land use planning to manage water resources in Europe

Estimation of kinematic parameters in a model of artificial aortic valve leaflets

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