An improved sequential DeepONet model implementation that uses a recurrent neural network (GRU) in the branch and a feed-forward neural network in the trunk. It can predict the full field solutions at multiple time steps given a time-dependent input function and the domain.
The DeepONet implementation and training is based on DeepXDE: @article{lu2021deepxde, title={DeepXDE: A deep learning library for solving differential equations}, author={Lu, Lu and Meng, Xuhui and Mao, Zhiping and Karniadakis, George Em}, journal={SIAM review}, volume={63}, number={1}, pages={208--228}, year={2021}, publisher={SIAM} }
If you find our model helpful in your specific applications and researches, please cite this article as: To be updated.
The training data is large in size and can be downloaded through the following UIUC Box link: https://uofi.app.box.com/s/g609d6x43cvi6ylr7zhj7vazd9tdfix2