Deepxde

DeepXDE supports physics-informed DeepONet.🎉🎉🎉

Areas of improvement

- Fix the issue when setting random seed via `dde.set_random_seed()`

New APIs

- Support physics-informed DeepONet
- Add function spaces: `dde.data.PowerSeries`, `dde.data.Chebyshev`, `dde.data.GRF`, `dde.data.GRF_KL`, `dde.data.GRF2D`
- Add `dde.data.PDEOperator`
- Add `dde.nn.PIDeepONet`

Areas of improvement

- Fix `is_on_line_segment()` such as `Polygon.boundary_normal()` works for float32
- Refactor backend JAX: utilize `vmap`, and add auxiliary arguments to data.losses

New APIs

- Add `NN.num_trainable_parameters()`

API changes

- `dde.data.MfDataSet` adds a new argument `standardize=False`, which changes the default behavior of `MfDataSet`. In the previous version, "standardize" is used by default.

Areas of improvement

- Backend JAX supports Jacobian
- Backend JAX uses a random random seed, and supports user-defined random seed
- MFNN test loss returns 0 instead of nan
- Bug fix: CSG for float32

API changes

- Move saving and plotting functions to utils.external. Delete deepxde/postprocessing.py

Areas of improvement

- JAX backend supports different optimizers
- JAX backend: improve `FNN` to fully support function approximation
- Improve code format, and code cleaning
- Bug fix: float64
- Documentation and examples improvements

DeepXDE supports JAX backend for function approximation. 🎉🎉🎉

Areas of improvement

- Bug fix: float64 in PyTorch
- Documentation and examples improvements

New APIs

- Add `PODDeepONet`
- Support MIONet: `Quadruple`, `QuadrupleCartesianProd`, `MIONet`, `MIONetCartesianProd`

API changes

- Rename `dde.icbcs` to `dde.icbc`