Code supporting the paper: 1D neural network

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.

本在线资源包含两个归档文件夹：<em>Matlab</em>和<em>Python</em>，其中存放着与文章《一种基于贝叶斯有限元训练的机器学习方法用于预测烧伤后收缩》相关的代码。<br>在<em>Matlab</em>文件夹中可找到用于生成大型数据集的代码。该文件夹中的<em>Main.m</em>为主文件，通过它可运行蒙特卡洛模拟，此外还有一个README文件。<br>在<em>Python</em>文件夹中，存放着用于训练神经网络和创建在线应用的代码。<em>Data.mat</em>文件包含由Matlab蒙特卡洛模拟生成的数据。<em>run_bound.py、run_rsa.py</em>和<em>run_tse.py</em>文件用于训练神经网络，其中得分最高的模型保存在<em>Training</em>文件夹中。<em>DashApp</em>文件夹包含应用创建的代码。