DeepXDE v0.13.6

Areas of improvement

• Bug fix: Model.predict() works for DeepONet

DeepXDE v0.13.5

Areas of improvement

• TensorFlow 2.x and PyTorch support loss_weights
• Improve L-BFGS for TensorFLow 2.x and PyTorch
• Geometry.random_boundary_points() doesn't sample corner points

New APIs

• Add dde.optimizers.set_LBFGS_options()

DeepXDE v0.13.4

Areas of improvement

• Backend TensorFlow 2.x supports L-BFGS via TFP
• Backend PyTorch supports L-BFGS
• Backend PyTorch uses GPU by default, if available
• Improve BC/IC performance for backend PyTorch

New APIs

• Add dde.Variable for inverse problems

DeepXDE v0.13.3

• Backend PyTorch supports PDE inverse problems

Areas of improvement

• 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

DeepXDE v0.13.2

• Backend PyTorch supports PDE forward problems

API changes

• Refactor uncertainty via MC dropout as the callback DropoutUncertainty; remove "uncertainty" argument from Model.train().

DeepXDE v0.13.1

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()

DeepXDE v0.13.0

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.

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()

DeepXDE v0.12.0

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'

DeepXDE v0.11.2

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()

DeepXDE v0.11.1

Areas of improvement

• FNN supports arguments use_bias and kernel_constraint, and layer normalization
• Change L-BFGS option gtol from 1e-5 to 1e-8
• Improve saveplot

New APIs

• Add a new Data Constraint
• Add metric mean_squared_error