Hierarchical Physics-Informed Neural Network Framework for 3D Magnetic Modeling of Medium Frequency Transformers
Hierarchical Physics-Informed Neural Network (HPINN) Framework is a typically designed tool for the 3D Magnetic Modeling of Medium Frequency Transformers (MFTs).
This respository includs all the necessary elements for training, testing, and using HPINN framework.
- Open a terminal and change directory to this program folder:
cd [path-to-folder]
- Clone the temperatur conversion repository:
git clone https://github.com/fleaxiao/HPINN.git
- Implementation order:
1. data_generation_2D -> train_2D_loss -> test_2D_loss
2. model_combine
3. data_generation_3D -> train_3D_loss -> test_3D_loss
4. Use_HPINN
If you use this code or data in your research, please cite our work as follows:
Xiao Yang, Liangcai Shu, Dongsheng Yang. Hierarchical Physics-Informed Neural Network Framework for 3D Magnetic Modeling of Medium Frequency Transformers. TechRxiv. April 29, 2024.