search

lululxvi / deepxde

A library for scientific machine learning and physics-informed learning
https://deepxde.readthedocs.io
GNU Lesser General Public License v2.1
2.5k stars 715 forks source link

Generalization of the network #282

Closed Mengting009 closed 2 years ago

Mengting009 commented 3 years ago

Hi Lu,

Thank you for sharing the library.

It seems that a trained network can only be used to predict the results of the same geometry with the same boundary condition and initial condition. Once any of the above-mentioned conditions is changed, the network has to be trained again for new predictions. I am wondering is there a possibility to generalize the approach such that once the network is trained, it can be used for predictions with different initial and boundary conditions? Thank you!

lululxvi commented 3 years ago

Yes, PINN is for specific IC/BC. If you want to train a network for different IC/BC, you can use these IC/BC as an extra network input, and train the network for different IC/BC. Another way is to use DeepONet, see https://doi.org/10.1038/s42256-021-00302-5

alokwarey commented 3 years ago

@lululxvi: Is there an example or sample code snippet on how to setup a problem for different IC/BC using PINNs with deepxde?

lululxvi commented 3 years ago

No good examples yet, but see #273