3D-ConvNeuralNet-material-property-prediction
说明： 本报告包含用于材料性能预测的3D卷积神经网络的数据集代码，
(This repo includes the dataset code of 3D Convolutional Neural Network for material property prediction,)
(This repo includes the dataset code of 3D Convolutional Neural Network for material property prediction,)
文件列表:
data/ (0, 2020-08-14)
data/download_link.txt (171, 2020-08-14)
gallery/ (0, 2020-08-14)
gallery/NN_archi.png (134417, 2020-08-14)
gallery/Pred_vs_Truth_test.png (1022656, 2020-08-14)
gallery/clous_pt.png (6481926, 2020-08-14)
saved_models/ (0, 2020-08-14)
saved_models/my_best_model_CNN_new.h5 (19178352, 2020-08-14)
training&plot.py (12961, 2020-08-14)
data/download_link.txt (171, 2020-08-14)
gallery/ (0, 2020-08-14)
gallery/NN_archi.png (134417, 2020-08-14)
gallery/Pred_vs_Truth_test.png (1022656, 2020-08-14)
gallery/clous_pt.png (6481926, 2020-08-14)
saved_models/ (0, 2020-08-14)
saved_models/my_best_model_CNN_new.h5 (19178352, 2020-08-14)
training&plot.py (12961, 2020-08-14)
# 3D-ConvNeuralNet-material-property-prediction
# Authors and citation
This repo includes the dataset/scripts of 3D Convolutional Neural Network for material property prediction in paper:
[Rao, Chengping, and Yang Liu. "Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization." Computational Materials Science 184 (2020): 109850.](https://www.sciencedirect.com/science/article/abs/pii/S0927025620303414)
```
@article{rao2020three,
title={Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization},
author={Rao, Chengping and Liu, Yang},
journal={Computational Materials Science},
volume={184},
pages={109850},
year={2020},
publisher={Elsevier}
}
```
# Description for each file
- **training&plot.py**: Data processing, training and postprocessing scripts ;
- **data**: The dataset for this application contains 2000 samples or (**X, Y**) pairs. **X** is the tensor of size 101x101x101 that carries the phase indication for a two-phase heterogeneous material microstructure (or Representative Volume Element). **Y** is a vector of size 12 that represents the effective material properties for the microstructure.
- **saved_model**: The model with best performance on validation set.
- **gallery**: Some figures.
# Overview
> Input **X** for the 3D Convolutional Neural Net, i.e. Cartisian grid carrying phase indication
> Architecture of the 3D Convolutional Neural Net
> Achieved prediction versus ground truth on testing data
# Note
- The code was developed based on TensorFlow 1.10.0 and Keras 2.2.4. All the runtime performance are evaluated on GeForce GTX 1080 Ti.
> Input **X** for the 3D Convolutional Neural Net, i.e. Cartisian grid carrying phase indication
> Architecture of the 3D Convolutional Neural Net
> Achieved prediction versus ground truth on testing data
# Note
- The code was developed based on TensorFlow 1.10.0 and Keras 2.2.4. All the runtime performance are evaluated on GeForce GTX 1080 Ti.
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