Limit state function identification using Support Vector Machines for discontinuous responses and disjoint failure domains

Basudhar, Anirban; Missoum, Samy; Sánchez, Alcides Antúnez

doi:10.1016/j.probengmech.2007.08.004

Cited by 126 publications

(68 citation statements)

References 18 publications

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“…Model error sampling for instance, samples in areas with most disagreements between the model and the actual outputs [45]. Model-based methods typically pursue a specific goal: EDSD (Explicit Design Space Decomposition) [46] for instance is a sequential design method for refining the class boundary of an SVM model, whereas Probability of Feasibility [47] searches for areas which exceed a certain threshold making them suitable for sampling constrained areas.…”

Section: Existing Sequential Sampling Methodsmentioning

confidence: 99%

“…OUTPUT-BASED MODEL-BASED Low discrepancy sequences [48,49,50] Probability of Feasibility (PoF) [47] Sequentially Nested Latin Hypercubes [51,52,17] (F)LOLA-Voronoi (Regression) [16,53] Model error sampling (regression) [45] Montecarlo/Optimization Based [14,15] NeighbourhoodVoronoi (Classification) [54] D-and G-optimal designs [55,56] Voronoi-based [16] Explicit Design Space Decomposition (EDSD) [46] Random Sequential Exploratory Experimental Design (SEED) [42] (an output-based approach) and Probability of Feasibility [47] (a model-based approach). These methods are also applied to the test cases in Section 5.…”

Section: Input-basedmentioning

confidence: 99%

“…Furthermore the N-V algorithm automatically balances exploration and exploitation which allows discovery of previously undiscovered class regions. The latter property distinguishes N-V from other methods such as EDSD [46], which assumes the initial set of points finds all regions. A downside of the N-V algorithm is its increasing computational complexity as the dimensionality of the input space grows (similar to the problems encountered with the LOLA-Voronoi algorithm).…”

Section: Neighbourhood-voronoimentioning

confidence: 99%

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Adaptive classification under computational budget constraints using sequential data gathering

Herten

Couckuyt

Deschrijver

et al. 2016

Advances in Engineering Software

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Classification algorithms often handle large amounts of labeled data. When a label is the result of a very expensive computer experiment (in terms of computational time), sequential selection of samples can be used to limit the overall cost of acquiring the labeled data. This paper outlines the concept of sequential design for classification, and the extension of an existing stateof-the-art research platform for surrogate modeling to handle classification problems with sequential design. The capabilities of the platform are illustrated on a number of use cases including real-world applications such as an ElectroMagnetic Compatibility (EMC) and a Computational Fluid Dynamics (CFD) problem. The CFD problem also illustrates how classification can be used together with regression techniques to solve multi-objective constrained optimization problems of complex systems.

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Section: Existing Sequential Sampling Methodsmentioning

confidence: 99%

Section: Input-basedmentioning

confidence: 99%

Section: Neighbourhood-voronoimentioning

confidence: 99%

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Adaptive classification under computational budget constraints using sequential data gathering

Herten

Couckuyt

Deschrijver

et al. 2016

Advances in Engineering Software

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“…The latter feature is essential for problems with discontinuous behaviors or binary behaviors (accept or reject). This was demonstrated by the first author for crashworthiness and buckling problems [15,16] as well as biomedical device design [17]. The explicit boundaries are created using a support vector machine (SVM) that can create multidimensional, nonlinear, and disjoint boundaries.…”

Section: Introductionmentioning

confidence: 99%

“…These estimates can then be used to perform reliability-based design optimization (RBDO) [15] as described in Sec. V.…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Design Optimization of Nonlinear Aeroelasticity Problems

Missoum

Dribusch

Beran

2010

Journal of Aircraft

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This paper introduces a methodology for the reliability-based design optimization of systems with nonlinear aeroelastic constraints. The approach is based on the construction of explicit flutter and subcritical limit cycle oscillation boundaries in terms of deterministic and random design variables. The boundaries are constructed using a support vector machine that provides a way to efficiently evaluate probabilities of failure and solve the reliabilitybased design optimization problem. Another major advantage of the approach is that it efficiently manages the discontinuities that might appear during subcritical limit cycle oscillations. The proposed approach is applied to the construction of flutter and subcritical limit cycle oscillation boundaries for a two-degree-of-freedom airfoil with nonlinear stiffnesses. The solution of a reliability-based design optimization problem with a constraint on the probability of subcritical limit cycle oscillation is also provided.

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