Multilevel surrogate modeling approach for optimization problems with polymorphic uncertain parameters

DOI10.1016/J.IJAR.2019.12.015zbMath1440.90040OpenAlexW2997857754WikidataQ126405147 ScholiaQ126405147MaRDI QIDQ2300448

Katharina Kremer, Steffen Freitag, Philipp Edler, Günther Meschke

Publication date: 27 February 2020

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2019.12.015

zbMATH Keywords

optimizationartificial neural networksurrogate modelreinforced concrete structurepolymorphic uncertainty

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Robustness in mathematical programming (90C17)

Related Items (2)

Polymorphic uncertainty quantification for engineering structures via a hyperplane modelling technique ⋮ Adaptive reliability analysis for rare events evaluation with global imprecise line sampling

Cites Work

• Unnamed Item
• Unnamed Item
• A survey on approaches for reliability-based optimization
• Robust optimization - a comprehensive survey
• Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation
• Fuzzy structural analysis using \(\alpha\)-level optimization
• Reliability-based structural optimization using neural networks and Monte Carlo simulation
• An anisotropic elastoplastic-damage model for plain concrete

This page was built for publication: Multilevel surrogate modeling approach for optimization problems with polymorphic uncertain parameters