Deepxde

Backend JAX supports PDE inverse problems.🎉🎉🎉

Areas of improvement

- Backend PyTorch supports l2 regularization
- Improve XLA jit

New APIs

- Add `dde.data.PDEOperatorCartesianProd`
- Backend TensorFlow supports `dde.nn.PFNN`

API changes

- `Model.train`: `epochs` is replaced by `iterations`
- Merge `PIDeepONet` into `DeepONet`

Areas of improvement

- Bug fix: `dde.icbc.NeumannBC` for `dde.geometry.Polygon`

New APIs

- Add new geometry `dde.geometry.PointCloud`

Areas of improvement

- Support new loss "mean l2 relative error"
- Backend PyTorch supports `dde.nn.DeepONetCartesianProd` and `dde.nn.PODDeepONet`
- Backend PyTorch supports "step" LR decay
- `dde.callbacks.OperatorPredictor` supports backends tensorflow.compat.v1, tensorflow, pytorch, paddle
- Fix `dde.config.set_random_seed`

New APIs

- Add `dde.config.enable_xla_jit` and `dde.config.disable_xla_jit`

API changes

- Change `DDEBACKEND` to `DDE_BACKEND`

DeepXDE supports a new backend PaddlePaddle.🎉🎉🎉

Areas of improvement

- Backend PyTorch supports `dde.nn.PFNN`
- Backend TensorFlow 1.x enables XLA for GPU

This is a bugfix release.

Areas of improvement

- Bug fix: `NeumannBC` and `RobinBC`

Backend JAX supports ODE forward problems.🎉🎉🎉

Areas of improvement

- Backend TensorFlow supports `model.save` and `model.restore`.
- Backend TensorFlow uses `tf.function(jit_compile=True)` for faster speed.