Code supporting the paper: High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations
This online resource shows three archived folders: Matlab, Python, and App that contain relevant code and data for the article: High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations.
Within the Matlab folder, one finds the codes used for the generation of the large dataset. Here, the file Main.m is the main file and from there, one can run the Monte Carlo simulation.
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.
Within the App folder, one finds the executable nn_R2_app.exe that one can run, once the archived folder is unzipped. When running the app, it opens in a browser. This was checked in Windows.
- 2023-01-30 first online, published, posted
- Dutch Burn Foundation, project 17.105.
University of Hasselt, Department of Mathematics and Statistics;
Burn Centre and Department of Plastic, Reconstructive and Hand Surgery, Red Cross Hospital, Beverwijk, Netherlands;
Department of Plastic, Reconstructive and Hand Surgery, Amsterdam UMC;
Pediatric Surgical Centre, Emma Children's Hospital, Amsterdam UMC
- 90,585 bytes md5 Matlab.zip
- 3,140,468,499 bytes md5 Python.zip
- 170,693,865 bytes md5 app.zip
- download all files (zip)