Comments (1)
PINNs were originally proposed to replace PDE solvers, meaning no analytical solution is required. This is very different from conventional supervised PDE solvers such as unet, FEDformer, FNO where you actually provide the analytical solution for each sample.
Theoretically, a PINN should work just like a PDE solver. But in practice, there are so many problems that an accurate solution is not guaranteed. You need to tinker the hyperparameters.
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Related Issues (20)
- How to give different inputs for subnetworks in PFNN
- Polygon PeriodicBC Problem HOT 1
- Model doesnot converge even after trying everything HOT 2
- possible bug: 'targets' variable not used in PDE losses() method?
- Hyperelasticity HOT 2
- Boundary condition as input for branch net in PI DeepONet
- ImportError: cannot import name 'check_pandas_support' from 'sklearn.utils' HOT 1
- ImportError check_pandas_support HOT 4
- Operator prediction becomes constant
- PDE error evaluated on bc points in PDE.losses but not used by following codes
- Is there a demo/code/model available for Fourier-MIONet?
- PDE with additional parameters HOT 8
- Support for Data Loader HOT 1
- Setting Custom Initial Weights in a Neural Network
- Implementing a free slip BC on a cylinder HOT 1
- multiple inputs and mixed data inputs by DEEPXDE
- Save only the best model files
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from deepxde.