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Dynamic Pulling Methods(DPM)

This is the official code for paper:

We present new algorithm named DPM (Dynamic Pulling Methods). This idea is originated from PINNs(Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational Physics 378 (2019): 686-707.). While observing the capability of PINN as a tool for learning the dynamics of physical processes, we discovered that the accuracy of the approximate solution produced by PINN in an extrapolation setting is significantly reduced compared to that proceed in an extrapolation setting. Motivated by this observation, we propose our method to improve the approximation accuracy in extrapolation and to demonstrate the effectiveness of the proposed method with various benchmark problems. This means that our models is well learned governing equation of Parital Differential Equations.

Installation

  • Clone this repository and enter it: 
    git clone [email protected]:jekim5418/DPM.git
  • Set up the environment using one of the following methods:
    • Using Anaconda
      • Run conda env create -f environment.yml
    • Manually with pip
      • Set up a Python3 environment (e.g., using virtenv).
      • Install Tensorflow 1.14
      • Install some other packages: 
        pip install cython
pip install opencv-python pillow pycocotools matplotlib

Train

  • If you'd like to train DPM, all of data is in data foler. Just run main model.
  • set hyperparameters that you want(num_layers, num_neurons, learning_rate, limits, epsilon) 
    # Viscous Burgers
python3 Burgers_parameter_opt_Adam_con_limit_ResNet_DPM.py --num_layers=[num_layers] --num_neurons=[num_neurons] --learning_rate=[learning_rate] --epsilon=[epsilon] --delta=[delta] --w=[w]
# Inviscid Burgers
python3 Inviscid_burgers_parameter_opt_Adam_con_limit_ResNet_DPM.py --num_layers=[num_layers] --num_neurons=[num_neurons] --learning_rate=[learning_rate] --epsilon=[epsilon] --delta=[delta] --w=[w]
# Schrodinger
python3 Schrodinger_parameter_opt_Adam_con_limit_ResNet_DPM.py --num_layers=[num_layers] --num_neurons=[num_neurons] --learning_rate=[learning_rate]  --epsilon=[epsilon] --delta=[delta] --w=[w]
# Allen-Cahn
python3 AC_parameter_opt_Adam_con_limit_ResNet_DPM.py --num_layers=[num_layers] --num_neurons=[num_neurons] --learning_rate=[learning_rate]  --epsilon=[epsilon] --delta=[delta] --w=[w]
  • At each equation folder, there is 'equation_parameter_opt_Adam_weighted.py'(ex. Schrodinger_parameter_opt_Adam_weighted.py) file and it is a code for original PINN training in extrapolation. Also, 'equation_parameter_opt_Adam_weighted_ResNet.py'(ex. Schrodinger_parameter_opt_Adam_weighted_ResNet.py) file is for ResNet PINN.

Evaluation

  • Best hyperparameter is described in below. Here are our best hyperparameters of DPM models:
Equation num_layers num_neurons learning rate epsilon delta w
Viscous Burgers 8 20 0.005 0.001 0.08 1.001
Inviscid Burgers 8 20 0.01 0.0123 1.00 1.0019
Schrodinger 3 50 0.001 0.003 0.05 1.029
Allen-Cahn 6 100 0.0005 0.001 0.01 1.022
  • For each equation folder, there exists tf_model directory which contains best hyperparameter checkpoints. If you want to evaluate our model, then load checkpoints that I saved. Also, I saved original-PINN and ResNet-PINN checkpoints in according directory. So it might be possible to compare performance between those two models and ours(DPM).

Comparison of performance in extrapolation between benchmark model and ours(DPM):

  • L2-norm error(lower is better):
Equation Original PINN ResNet PINN DPM
Viscous Burgers 0.329 0.333 0.092
Inviscid Burgers 0.131 0.095 0.083
Schrodinger 0.350 0.286 0.182
Allen-Cahn 0.239 0.212 0.141
  • Explained Variance Score(higher is better):
Equation Original PINN ResNet PINN DPM
Viscous Burgers 0.891 0.901 0.991
Inviscid Burgers 0.214 0.468 0.621
Schrodinger 0.090 0.919 0.967
Allen-Cahn -4.364 -3.902 -3.257
  • Max Error(lower is better):
Equation Original PINN ResNet PINN DPM
Viscous Burgers 0.657 1.081 0.333
Inviscid Burgers 3.088 2.589 1.534
Schrodinger 1.190 1.631 0.836
Allen-Cahn 4.656 4.222 3.829
  • Mean Absolute Error(lower is better):
Equation Original PINN ResNet PINN DPM
Viscous Burgers 0.085 0.108 0.021
Inviscid Burgers 0.431 0.299 0.277
Schrodinger 0.212 0.142 0.094
Allen-Cahn 0.954 0.894 0.868

Citation

@misc{kim2020dpm,
      title={DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation}, 
      author={Jungeun Kim and Kookjin Lee and Dongeun Lee and Sheo Yon Jin and Noseong Park},
      year={2020},
      eprint={2012.02681},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contact

For questions about our paper or code, please contact to Jungeun Kim or Noseong Park.