DeepXDE v1.1.2

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

DeepXDE v1.1.1

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 v1.1.0

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

DeepXDE First Stable Release v1.0.0

DeepXDE was developed starting from the summer of 2018, self-hosted in Subversion at Brown University, originally under the name SciCoNet (Scientific Computing Neural Networks). On Feb 7, 2019, SciCoNet was moved from Subversion to GitHub, renamed to DeepXDE. The first version v0.1.0 was then released on Jun 12, 2019. After the development of more than three years and a half, DeepXDE with backend TensorFlow 1.x becomes stable, and thus we now release the first stable version v1.0.0! 🎉🎉🎉

Thank you all for taking the time to contribute! A non-exhaustive but growing list needs to mention: @lululxvi @smao-astro @ZongrenZou @Saransh-cpp @anranjiao @pescap @Handi-Zhang @Anilith etc.

DeepXDE already has partial support of backend TensorFlow 2.x (from v0.13.0, Jul 21, 2021) and PyTorch (from v0.13.1, Jul 28, 2021). The multiple backend (also JAX) support will be enhanced in DeepXDE v1.x, and more advanced features (e.g., PINN-DeepONet) will be developed.

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Here are the new improvements, compared to the previous version.

Areas of improvement

• Stop training if loss becomes nan (#491)
• EarlyStopping callback can monitor testing loss (#501)
• Documentation improvements

New APIs

• Add set_random_seed to set the global random seed (#495)
• Add function dat_to_csv to convert dat files to CSV format (#499)

DeepXDE v0.14.1

Change license from Apache-2.0 to LGPL-2.1

Areas of improvement

• callback MovieDumper supports backend TensorFlow and PyTorch

New APIs

• Add PDE.replace_with_anchors()

DeepXDE v0.14.0

We stop the support of Python 3.6 from this release.

Areas of improvement

• Model.save() and Model.restore() supports backend PyTorch
• Model.predict() supports PDE auxiliary variables for backend TensorFlow 1.x and TensorFlow 2.x
• Bug fix on some double/float issues
• Documentation improvements

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