Two multifidelity kriging-based strategies to control discretization error in reliability analysis exploiting a priori and a posteriori error estimators

Mell, Ludovic; Rey, Véronique; Schoefs, Franck

doi:10.1016/j.compstruc.2022.106897

Cited by 3 publications

(2 citation statements)

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“…Discretization error estimators are available in the literature [16] and enable computing the error on a quantity of interest of the mechanical problem. Those estimators were exploited in the context of relibility within FORM [22] and kriging [23]. However, those approaches are limited to certain type of meta-models and do not include SVM.…”

Section: Introductionmentioning

confidence: 99%

Support vectors machines for the estimation of probability of failure: Multifidelity classifiers built from a posteriori discretization error estimators

2023

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Section: Introductionmentioning

confidence: 99%

Support vectors machines for the estimation of probability of failure: Multifidelity classifiers built from a posteriori discretization error estimators

2023

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“…The first group is more generic and directly applicable to a variety of problems, thereby, it is also more popular. Some of the widely used modeling techniques include neural networks [58][59][60], kriging [61], radial basis functions [62], polynomial chaos expansion [63], support vector regression [64], etc. Datadriven surrogates are used for global [65] and multi-objective design [66] and statistical analysis [67], and they are often combined with machine learning methods [68].…”

Section: Introductionmentioning

confidence: 99%

Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction

2023

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The design of contemporary antenna systems encounters multifold challenges, one of which is a limited size. Compact antennas are indispensable for new fields of application such as the Internet of Things or 5G/6G mobile communication. Still, miniaturization generally undermines electrical and field performance. When attempted using numerical optimization, it turns into a constrained problem with costly constraints requiring electromagnetic (EM) simulations. At the same time, due to the parameter redundancy of compact antennas, size reduction poses a multimodal task. In particular, the achievable miniaturization rate heavily depends on the starting point, while identifying a suitable starting point is a challenge on its own. These issues indicate that miniaturization should be addressed using global optimization methods. Unfortunately, the most popular nature-inspired algorithms cannot be applied for solving size reduction tasks because of their inferior computational efficacy and difficulties in handling constraints. This work proposes a novel methodology for the globalized size reduction of antenna structures. Our methodology is a multi-stage knowledge-based procedure, initialized with the detection of the approximate location of the feasible region boundary, followed by the construction of a dimensionality-reduced metamodel and global optimization thereof; the last stage is the miniaturization-oriented local refinement of geometry parameters. For cost reduction, the first stages of the procedure are realized with the use of a low-fidelity EM antenna model. Our approach is verified using four broadband microstrip antennas and benchmarked against multi-start local search as well as nature-inspired methods. Superior size reduction rates are demonstrated for all considered cases while maintaining reasonably low computational costs.

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Hybrid uncertainty propagation based on multi-fidelity surrogate model

Liu,

Shi,

Ding

et al. 2024

Computers & Structures

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Two multifidelity kriging-based strategies to control discretization error in reliability analysis exploiting a priori and a posteriori error estimators

Cited by 3 publications

References 52 publications

Support vectors machines for the estimation of probability of failure: Multifidelity classifiers built from a posteriori discretization error estimators

Support vectors machines for the estimation of probability of failure: Multifidelity classifiers built from a posteriori discretization error estimators

Globalized Knowledge-Based, Simulation-Driven Antenna Miniaturization Using Domain-Confined Surrogates and Dimensionality Reduction

Hybrid uncertainty propagation based on multi-fidelity surrogate model

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