Comments (1)
Better reparameterization seems to do the trick.
A better solution can be obtained by using a Dirichlet Condition
that fixes one end of the boundary and penalizes the boundary value on the other end (by adding an additional term to the loss).
from neurodiffeq.
Related Issues (20)
- Use a single network for ODE/PDE systems HOT 2
- Unable to import solver and monitor HOT 6
- High Order Optimizers HOT 4
- Special Type Boundary Condition HOT 4
- Is there a way to access the train/valid loss history for Solver1D, like for solve? HOT 2
- Using Special type activation function HOT 9
- Finding value of weights HOT 2
- Saving subclass of solvers HOT 1
- TqdmKeyError: "Unknown argument(s): {'colour': 'blue'}" HOT 3
- Publish `neurodiffeq` on `conda-forge`?
- Neumann boundary conditions
- Nonzero Dirichlet boundary conditions HOT 8
- Add tests for solver_utils
- BundleSolver setup too restrictive HOT 1
- fitting a variable in a system of ODE as a function of time HOT 4
- Problems :return inspect.signature(optimizer.step).parameters.get('closure').default == inspect._empty AttributeError: 'NoneType' object has no attribute 'default' HOT 2
- Parametric system of ODEs HOT 1
- Bundle Solution for PDEs
- Improve Docs
- Solving system of PDEs/ODEs HOT 1
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from neurodiffeq.