This is the AD for the paper: Physics-informed Quantum Deep Neural Network for Solving PDEs.
The structure of the PIQDNN is illustrated as follows:
The source code for building the model and training, and the illustration of the model can be found unde: /src.
The training dataset can be found in: data/burger, data/poisson
The results of some of the experiments can be found in: /results
Another repository is created for the code used to generate data for Poisson equation, which can be found in: https://github.com/qifengpan/2D-ShwarzSolver-for-Poisson
The configuaration for the environment can be found in requirement.txt, the docker image for this implementation is in preparation, could be provided upon request. run the training:
running the Poisson model:
python3 src/main.py --problem-type Poisson
running the Burger model:
python3 src/main.py --problem-type Burger
The model detail can be modified within the main.py file.