- Refactor some parts of `Model` - Bug fix for backend TensorFlow 2.x and PyTorch
API changes
- Rename `dde.maps` to `dde.nn` to be more explicit
0.13.2
- Backend PyTorch supports PDE forward problems
API changes
- Refactor uncertainty via MC dropout as the callback `DropoutUncertainty`; remove "uncertainty" argument from `Model.train()`.
0.13.1
DeepXDE supports PyTorch backend for function approximation.
Areas of improvement
- Backend TensorFlow supports auxiliary variables
New APIs
- Add `dde.config.set_default_float()` and `dde.config.default_float()`
0.13.0
DeepXDE now supports two backends: TensorFlow 1.x (`tensorflow.compat.v1` in TensorFlow 2.x) and TensorFlow 2.x. For how to select one, see [Working with different backends](https://deepxde.readthedocs.io/en/latest/user/installation.html#working-with-different-backends).
Areas of improvement - Many modules are refactored to better support multiple backends. - Support skopt>=0.9 - Documentation improvements
API changes - Rename `dde.data.Func` to `dde.data.Function`
New APIs - Add `Hypercube.random_boundary_points()`
0.12.0
This release is mainly about DeepONet.
API changes
- Rename `OpNN` to `DeepONet` - Rename `OpDataSet` to `Triple`
New APIs
- Add `dde.__version__` - Add `data.TripleCartesianProd`, `maps.DeepONetCartesianProd`, and `maps.FourierDeepONetCartesianProd` - Add new metric: `mean_l2_relative_error`
Areas of improvement
- Bug fix: change 'sobol' to 'Sobol'
0.11.2
Areas of improvement
- Add Multi-scale Fourier Feature Neural Networks: `MsFFN` and `STMsFFN` - `PDE` supports more sampling methods: LHS, Halton, Hammersley - `DeepONet` supports input_transform and output_transform - `PointSet` supports default value - `Hypercube.boundary_normal()` returns averaged normal for vertices - Speedup `Polygon.random_points()`