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PIOperator:

some physics informed neural operators in classic fluid flows, and all models will use Pytorch framwork

Data:

1.Burgers 1d and Burgers 2d flow

2.Darcy flow

3.NS equation 2d

tips: more datasets will be added

Operator Method(pytorch):

1.DeepONet

2.POD-DeepONet

3.PI-DeepONet

4.FNO

5.PI-FNO

reference:

Li, Zongyi, et al. "Physics-informed neural operator for learning partial differential equations." ACM/JMS Journal of Data Science (2021).

Lu, Lu, et al. "Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators." Nature machine intelligence 3.3 (2021): 218-229.

Wang, Sifan, Hanwen Wang, and Paris Perdikaris. "Learning the solution operator of parametric partial differential equations with physics-informed DeepONets." Science advances 7.40 (2021): eabi8605.

Lu, Lu, et al. "A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data." Computer Methods in Applied Mechanics and Engineering 393 (2022): 114778.