Deepxde

Areas of improvement

- Add 2D interface boundary condition `dde.icbc.Interface2DBC`
- Backend JAX: Support loss weights
- Backend JAX: Support `dde.nn.PFNN`
- Backend JAX: Support `dde.callbacks.OperatorPredictor`
- Backend JAX: Fix input and output transform
- Add new examples in docs

Thanks to all the contributors to this release!

lululxvi kuangdai HydrogenSulfate bonneted jdellag vl-dud SebastianCobaise

- DeepXDE stops the support of Python 3.8 from this release.
- Many exciting new functions of automatic differentiation (AD) are added.

Areas of improvement

- `dde.grad` supports forward-mode AD for backends TensorFlow 1.x and 2.x, PyTorch, JAX. Use `dde.config.set_default_autodiff` to select.
- `dde.grad.jacobian` allows both `i` and `j` are None
- Backend PyTorch: DeepONet supports multiple outputs

New APIs

- Support new AD method in `dde.zcs`: Zero Coordinate Shift (ZCS), see https://arxiv.org/abs/2311.00860

Areas of improvement

- Refactor `dde.grad` module
- Backend TensorFlow 1.x and 2.x: `DeepONet` & `DeepONetCartesianProd` support multiple outputs
- Backend TensorFlow: Add regularization to `DeepONet`
- Backend PyTorch: Bug fix of `MIONet` `input_transform`
- Backend JAX: Support more PINN examples
- Backend JAX: Bug fix of `dde.grad`

Areas of improvement

- `dde.geometry.PointCloud` supports `boundary_normal`
- Backend pytorch: Allow L-BFGS line search
- Backend pytorch: Update GPU code to support pytorch 2.1.0

Areas of improvement

- Improve float32/float16 compatibility
- Improve examples and documents
- Backend TensorFlow: Support DeepONet and PI-DeepONet
- Backend PyTorch: Support PI-DeepONet
- Backend PyTorch: Bug fix and support more functions
- Backend Paddle: Support PI-DeepONet
- Backend Paddle: Support batch_size in PointSetBC

Areas of improvement

- Add new geometry `dde.geometry.StarShaped`
- Add approximate distance functions for hard-constraint methods
- Backend pytorch: Support `auxiliary_variables`
- Add many PI-DeepONet examples
- Improve documentation