Bi-fidelity stochastic gradient descent for structural optimization under uncertainty

DOI10.1007/s00466-020-01870-wzbMath1465.74137arXiv1911.10420OpenAlexW3047494561WikidataQ113326923 ScholiaQ113326923MaRDI QIDQ2221705

Alireza Doostan, Subhayan De, Kurt Maute

Publication date: 2 February 2021

Published in: Computational Mechanics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.10420

zbMATH Keywords

convergencetopology optimizationcontrol variatestochastic variance reduced gradientbi-fidelity methodstochastic average gradient

Mathematics Subject Classification ID

Stability and convergence of numerical methods for boundary value problems involving PDEs (65N12) Topological methods for optimization problems in solid mechanics (74P15) Stochastic and other probabilistic methods applied to problems in solid mechanics (74S60)

Related Items (6)

Neural network training using \(\ell_1\)-regularization and bi-fidelity data ⋮ MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources ⋮ Robust topology optimization with low rank approximation using artificial neural networks ⋮ A novel univariate dimension‐reduction based interval finite element method for static response prediction of uncertain structures ⋮ Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets ⋮ A stochastic subspace approach to gradient-free optimization in high dimensions

Uses Software

• Matlab
• FEniCS
• AdaGrad
• Adam
• top.m
• top88.m
• ADADELTA

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