Deepxde

Areas of improvement

- `PDE` supports excluded points
- Add `PointSetBC` for training using exact solution.
- Add `Hypersphere.boundary_normal()`
- Add `Triangle.inside()`

API changes

- `PDE` doesn't support numpy.array `X` as the third parameter. To define pde with extra coefficients, use `PDE(..., auxiliary_var_function=...)`, see an example at Lorenz_inverse_forced_Colab.ipynb.

Areas of improvement

- `Model.predict()` supports PDE with two or three arguments
- `PDE` supports different loss functions for different errors
- `Hypercube.uniform_points()` uses the same dx in each dimension

New APIs

- Add metric: `nanl2_relative_error`, `zero`
- Add callback: `Timer`

New module

- Add new module `dde.grad` to compute gradients via `dde.grad.jacobian()` and `dde.grad.hessian()`

Areas of improvement

- Bug fix: `GeometryXTime.periodic_point()`

Areas of improvement

- `Model.save()` supports pickle
- `PDE`
- supports `pde` as `None`
- supports `bcs` as the empty list `[]`
- accepts `train_distribution` = 'uniform' or 'pseudo' or 'sobol'
- `OpNN` supports user-defined branch net
- `OpDataSet` supports mini-batch training

API changes

- Rename `Model._print_model()` to `Model.print_model()`

New APIs

- Add `Model.state_dict()`
- Add `PDE.resample_train_points()`

Areas of improvement

- Add `Triangle.on_boundary` and `Triangle.boundary_normal`
- `OpNN` supports different activations for branch and trunk nets
- Bug fix: `GeometryXTime.uniform_points`

Areas of improvement

- Support numpy inputs to define PDE
- Bug fix: Hypershpere sobol points

New APIs

- Add feed-forward neural networks (`PFNN`) that support sub-networks
- Add fractional PDE (`FPDE` and `TimeFPDE`) with fractional Laplacian