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/f2a20379-0d48-4829-a5a2-c080eb669663

Storm, Joep (2024): Data underlying the publication: A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/f2a20379-0d48-4829-a5a2-c080eb669663.v1

Dataset

• Civil Engineering
• Mechanical Engineering
• Materials Engineering
• Engineering

Finite Element, Graph Neural Network, Multiscale, Surrogate

CC BY 4.0

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

by Joep Storm

Data accompanying the code in: https://github.com/JoepStorm/Microscale-GNN-Surrogate. The data contains results from multiscale finite element simulations and is aimed at training surrogate models. The data was generated with an in-house Finite Element software developed using the Jem/Jive open-source C++ library.

• 2024-02-13 first online, published, posted

4TU.ResearchData

text data in csv files, mesh files in .msh, images in .png and .pdf

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Materials, Mechanics, Management & Design (3MD), SLIMM AI LAB