PIRL for temperature field reconstruction of heat source systems

Introduction

This project provides the implementation of the paper "Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems". [paper]

Requirements

• Software
  • python
  • cuda
  • pytorch
• Hardware
  • GPU with at least 4GB

Environment construction

pip install -r requirements.txt

Datasets

The data used in this work can be generated by data generator.

Running

The training, test and visualization can be accessed by running main.py file.

• The data root is put in data_root in configuration file config/config.yml .

• Training

  python main.py -m train
  

  or

  python main.py --mode=train
  

• Test

  python main.py -m test --test_check_num=21
  

  or

  python main.py --mode=test --test_check_num=21
  

  or

  python main.py -m=test -v=21
  

  where variable test_check_num is the number of the saved model for test.

• Prediction visualization

  python main.py -m plot --test_check_num=21
  

  or

  python main.py --mode=plot --test_check_num=21
  

  or

  python main.py -m=test -v=21
  

  where variable test_check_num is the number of the saved model for plotting.

Project architecture

• config: the configuration file
  • data.yml describes the setups of the layout domain and heat sources
  • config.yml describes other configurations
• notebook: the test file for notebook
• outputs: the output results by test and plot module. The test results is saved at outputs/*.csv and the plotting figures is saved at outputs/predict_plot/.
• src: including surrogate model, training and testing files.
  • test.py: testing files.
  • train.py: training files.
  • plot.py: prediction visualization files.
  • DeepRegression.py: Model configurations.
  • data: data preprocessing and data loading files.
  • models: DNN surrogate models for the TFR-HSS task.
  • loss: physics-informed losses for training.
  • utils: useful tool function files.

• docker: start with docker.
• lightning_logs: saved models.

One tiny example

One tiny example for training and testing can be accessed based on the following instruction.

• Some training and testing data are available at samples/data.
• Based on the original configuration file, run python main.py directly for a quick experience of this tiny example.

Citing this work

If you find this work helpful for your research, please consider citing:

@article{gong2021,
    Author = {Zhiqiang Gong and Weien Zhou and Wei Peng and Wen Yao},
    Title = {Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems},
    Journal = {arXiv preprint arXiv:2106.11929},
    Year = {2021}
}