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)