Egberts, Ginger; Vermolen, Fred; Zuijlen, Paul van (2022): Code supporting the paper: 1D neural network. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/21407604.v1

Software

x choose version: version 2 - 2023-01-31 (latest)

• Applied Mathematics

Feed-forward neural network, Machine learning, Matlab, Medical application, Monte Carlo simulations, Morphoelasticity, Post-burn scar contraction, Python

CC BY-NC 4.0

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

by Ginger Egberts , Fred Vermolen , Paul van Zuijlen

This online resource shows two archived folders: Matlab and Python, that contain relevant code for the article: A Bayesian finite-element trained machine learning approach for predicting post-burn contraction. 

One finds the codes used to generate the large dataset within the Matlab folder. Here, the file Main.m is the main file and from there, one can run the Monte Carlo simulation. There is a README file.

Within the Python folder, one finds the codes used for training the neural networks and creating the online application. The file Data.mat contains the data generated by the Matlab Monte Carlo simulation. The files run_bound.py, run_rsa.py, and run_tse.py train the neural networks, of which the best scoring ones are saved in the folder Training. The DashApp folder contains the code for the creation of the Application.

• 2022-10-28 first online, published, posted

4TU.ResearchData

*.zip; *.m; *.mat; *.py; *.txt; *.png

A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

• Dutch Burn Foundation, project 17.105

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