Hamann, BenediktBenediktHamann2026-05-112026-05-112026-05-11https://hdl.handle.net/11420/62987This 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.enhttps://creativecommons.org/licenses/by-nc/4.0/Fail-Safe OptimizationArtificial Neural NetworksDamage ToleranceTechnology::620: EngineeringComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceDatasethttps://doi.org/10.15480/882.1707310.15480/882.17073