Failure Prediction and Robust Design of Grafts for Aortic Aneurysms

Layman, Ronald; Missoum, Samy; Geest, Jonathan Vande

doi:10.2514/6.2007-1888

Cited by 8 publications

(2 citation statements)

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“…The notion of explicit design space decomposition (EDSD) was introduced to circumvent the difficulties due to discontinuous behaviors (e.g., structural impact (Basudhar and Missoum 2009;Missoum et al 2007)) and binary problems (Missoum et al 2008;Layman et al 2007). Also, because the construction of explicit boundaries is based on designs of experiments (DOE), an adaptive sampling scheme was introduced to reduce the number of required function evaluations and the total computational cost .…”

Section: Introductionmentioning

confidence: 99%

A Multifidelity Approach for the Construction of Explicit Decision Boundaries: Application to Aeroelasticity

Dribusch

Missoum

Beran

2009

50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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This paper presents a multifidelity approach for the construction of explicit decision boundaries (constraints or limit-state functions) using support vector machines. A lower fidelity model is used to select specific samples to construct the decision boundary corresponding to a higher fidelity model. This selection is based on two schemes. The first scheme selects samples within an envelope constructed from the lower fidelity model. The second technique is based on the detection of regions of inconsistencies between the lower and the higher fidelity decision boundaries. The approach is applied to analytical examples as well as an aeroelasticity problem for the construction of a nonlinear flutter boundary.

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Section: Introductionmentioning

confidence: 99%

A Multifidelity Approach for the Construction of Explicit Decision Boundaries: Application to Aeroelasticity

Dribusch

Missoum

Beran

2009

50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

Self Cite

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“…The classification is performed using explicitly defined boundaries in space. A machine learning technique known as support vector machines (SVM) (Basudhar et al 2008;Layman et al 2007) is used to construct the boundaries separating distinct classes. The failure regions corresponding to different modes of failure are represented with a single SVM boundary, which is refined through adaptive sampling.…”

Section: Seismic Reliability Evaluation For Large Structuresmentioning

confidence: 99%