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  4. Dataset for Fail-Safe Topology Optimization using Artificial Neural Networks

Dataset for Fail-Safe Topology Optimization using Artificial Neural Networks

Citation Link: https://doi.org/10.15480/882.17073
Type
Dataset
Version
v1.0
Date Issued
2026-05-11
Author(s)
Hamann, Benedikt
Strukturmechanik im Leichtbau M-24
Researcher
Kriegesmann, Benedikt
Strukturmechanik im Leichtbau M-24
Language
English
TORE-DOI
10.15480/882.17073
TORE-URI
https://hdl.handle.net/11420/62987
Is Supplement To
10.1007/s00158-026-04362-2
10.15480/882.17314
Abstract
This repository contains the training dataset created within the research project "Structural Optimization for Fail-Safe Designs by Machine Learning". The objective of the project is to investigate machine learning approaches for fail-safe topology optimization. In particular, a conditional generative adversarial network (cGAN) was trained to map topology-optimized non-fail-safe structures obtained from standard compliance minimization to corresponding fail-safe designs.
Subjects
Fail-Safe Optimization
Artificial Neural Networks
Damage Tolerance
DDC Class
620: Engineering
006.3: Artificial Intelligence
Funding(s)
Strukturoptimierung ausfallsicherer Entwürfe durch maschinelles Lernen
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG)
License
https://creativecommons.org/licenses/by-nc/4.0/
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data.h5

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Hierarchical Data Format 5 File

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design_reshape.m

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Matlab

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structural_compliance.m

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5.18 KB

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README.md

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