A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty

Tan, Jingye; Faghihi, Danial

doi:10.1016/j.cma.2023.116628

Cited by 3 publications

(1 citation statement)

References 81 publications

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“…However, this method is highly intrusive and requires storing large matrices, which limits the scalability of the approach. A similar efficiency is achieved by Tan et al in [26], but again the resulting method is highly intrusive.…”

Section: Introductionsupporting

confidence: 65%

Robust design optimization using a non-intrusive second-order approximation of stochastic moments

Krüger,

Kriegesmann

2024

Preprint

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This paper presents a new formulation of the second-order fourth-moment method (sometimes referred to as second-order perturbation method or second-order method of moments). The method allows to efficiently predict the stochastic moments of a response function and is therefore often used within robust design optimization. The new approach allows a non-intrusive implementation at the same cost as existing, highly intrusive formulations. Therefore, the new approach can be applied to any objective function without significant implementation effort. It is based on a few finite difference steps into special directions an hence is dependent on the corresponding step sizes. An automatic step size procedure is supplied beside an detailed convergence analysis. The advantages of the new formulation are demonstrated by robust design optimizations of a 2D and a 3D example using the geometrically nonlinear finite element method.

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Section: Introductionsupporting

confidence: 65%

Robust design optimization using a non-intrusive second-order approximation of stochastic moments

Krüger,

Kriegesmann

2024

Preprint

View full text Add to dashboard Cite

show abstract

Robust design optimization using a non-intrusive second-order approximation of stochastic moments

Krüger,

Kriegesmann

2024

Struct Multidisc Optim

View full text Add to dashboard Cite

This paper presents a new formulation of the second-order fourth-moment method (sometimes referred to as second-order perturbation method or second-order method of moments). The method allows to efficiently predict the stochastic moments of a response function and is therefore often used within robust design optimization. The new approach allows a non-intrusive implementation at the same cost as existing, highly intrusive formulations. Therefore, the new approach can be applied to any objective function without significant implementation effort. It is based on a few finite difference steps into special directions and hence is dependent on the corresponding step sizes. An automatic step size procedure is supplied beside a detailed convergence analysis. The advantages of the new formulation are demonstrated by robust design optimizations of a 2D and a 3D example using the geometrically nonlinear finite element method.

show abstract

A framework for strategic discovery of credible neural network surrogate models under uncertainty

Singh,

Farrell-Maupin,

Faghihi

2024

Computer Methods in Applied Mechanics and Engineering

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A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty

Cited by 3 publications

References 81 publications

Robust design optimization using a non-intrusive second-order approximation of stochastic moments

Robust design optimization using a non-intrusive second-order approximation of stochastic moments

Robust design optimization using a non-intrusive second-order approximation of stochastic moments

A framework for strategic discovery of credible neural network surrogate models under uncertainty

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