cff-version: 1.2.0
abstract: "<p>This online resource shows two archived folders: <em>Matlab</em> and <em>Python</em>, that contain relevant code for the article: <em>A Bayesian finite-element trained machine learning approach for predicting post-burn contraction</em>. </p>
<p><br></p>
<p>One finds the codes used to generate the large dataset within the <em>Matlab</em> folder. Here, the file <em>Main.m</em> is the main file and from there, one can run the Monte Carlo simulation. There is a README file.</p>
<p><br></p>
<p>Within the <em>Python</em> folder, one finds the codes used for training the neural networks and creating the online application. The file <em>Data.mat</em> contains the data generated by the Matlab Monte Carlo simulation. The files <em>run_bound.py, run_rsa.py</em>, and <em>run_tse.py</em> train the neural networks, of which the best scoring ones are saved in the folder <em>Training</em>. The<em> DashApp</em> folder contains the code for the creation of the Application.</p>"
authors:
  - family-names: Egberts
    given-names: Ginger
    orcid: "https://orcid.org/0000-0003-3601-6496"
  - family-names: Vermolen
    given-names: Fred
    orcid: "https://orcid.org/0000-0003-2212-1711"
  - family-names: Zuijlen
    given-names: Paul van
    orcid: "https://orcid.org/0000-0003-3461-8848"
title: "Code supporting the paper: A Bayesian finite-element trained machine learning approach for predicting post-burn contraction"
keywords:
version: 2
identifiers:
  - type: doi
    value: 10.4121/21407604.v2
license: CC BY-NC 4.0
date-released: 2023-01-31