The code is a modification of the code for the work: physics informed neural networks by Raissi, Maziar at https://github.com/maziarraissi/PINNs under MIT License. The License is copied here:
MIT License
Copyright (c) 2018 maziarraissi
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Cells beginned with: def init_model, def get_grad, model = init_model(), from time import time, in the jupyter notebooks are modifications of some portions of the code at https://github.com/maziarraissi/PINNs and therefore follow the above copyright notice and permmision notice.
Deep learning (feedforward neural network) code is in the form of jupyter notebook using Tensorflow.
The main script for RBF interpolation is RBF_train_predict_modelname.m.
The code of generating the discrete reduced transfer function
is in the folder “compute_Vand_Reduced_transferfunction”.
The folder “training data generation” contains the code for computing the
training data and testing data for machine learning that can be directly used
as input data of the RBF and feedforward neural networks.