The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/17111648

Julian Schumann (2021): Some of the data underlying the publication "Fighting the curse of dimensionality: A machine learning approach to finding global optima". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/17111648.v1

Dataset

• Mechanical Engineering

autoencoder, global optimization, machine learning, topology optimization

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Julian Schumann

The data assembled here should allow the reproduction of the Figures 4 and 6 from the mentioned paper. The corresponding code can be found at https://github.com/julianschumann/ae-opt.

• 2021-12-03 first online, published, posted

4TU.ResearchData

.h5 .npy

TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3mE)