/RDCNN

Reaction diffusion system prediction based on convolutional neural network

Primary LanguageC++BSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

RDCNN

RDCNN predicts the result of reaction-diffusion system based on convolutional neural network (CNN)

User guide

User can run the code following the steps below.

1. FEM_Data_Geneartion (C++)

  • Description: this code is used to generate the dataset using FEM.

  • Input:

    • mesh_21_21.vtk (Quadrilateral mesh for domain geometry)

  • Output:

    • geometry_X_Y_input.txt (the input data storing boundary condition corresponding to Xth geometry and Yth parameter settings)
    • mesh_X.txt (the output data storing concentration results)
    • dataset_DKTGeo.txt (the library stores the parameter setting of each sample)

  • To compile: (requires Eigen)

    >> make

  • To run:

    Create a folder (eg. "\data") first and then create another 3 folders in this folder ("\data\input", "\data\output" and "\data\parametric") to store input data, output data and visualization results separately.

    >> ./rdfem -t <val_t> -s <val_s> -g <val_g> -o <output_path>

    output_path is the output path for data generation

    Example:

    >> ./rdfem -t 100 -s 500 -g 21 -o ../data/

2. txt_hdf5.ipynb

  • Description: This code is used to transform data format from TXT to H5
  • Input:
    • ./data (Dataset folder)
  • Output:
    • X.h5 (Dataset stored in h5 file)
  • To run： User can open the code in jupyter notebook follow the comments to run the code.

3. Main_data_rdcnn2larger.ipynb

  • Description: this code is used to train CNN model and predict concentration results for the specific reaction diffusion system.

  • To run： User can open the code in jupyter notebook follow the comments to run the code.