Code supporting the paper: A Bayesian finite-element trained machine learning approach for predicting post-burn contraction

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>. <br> 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. <br> 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.