The DOI displayed 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: 10.4121/69d1aefc-a01d-4280-8b32-5c8420d9a2a3

Husanović, Selma; Heinlein, Alexander; Egberts, Ginger; Vermolen, Fred (2024): Code supporting the paper: DeepONet models for predicting post-burn contraction. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/69d1aefc-a01d-4280-8b32-5c8420d9a2a3.v1

Usage statistics

Categories

• Applied Mathematics
• Numerical and Computational Mathematics
• Mathematical Sciences

Keywords

DeepONet Operator learning Post-burn contraction Surrogate model

Licence

CC BY 4.0

Interoperability

• RO-Crate Metadata

Export as...

RefWorks BibTeX Reference Manager Endnote DataCite NLM DC CFF

by Selma Husanović, Alexander Heinlein, Ginger Egberts , Fred Vermolen

This online resource provides the relevant data for the forthcoming article DeepONet models for predicting post-burn contraction. The archived folder "data.zip" contains MATLAB data files for training and evaluating the DeepONet models. These datasets were created using MATLAB code available at https://doi.org/10.4121/21257199. The Python code for handling the MATLAB data, training, and evaluating the DeepONet models is available at https://github.com/Selma24/DeepONet-contraction.

History

• 2024-10-30 first online, published, posted

Publisher

4TU.ResearchData

Format

zipped MATLAB files

References

• https://github.com/Selma24/DeepONet-contraction

Data link

https://doi.org/10.4121/21257199

Organizations

Delft University of Technology, Delft Institute of Applied Mathematics
University of Hasselt, Department of Mathematics and Statistics