Releases: NeuroDiffGym/neurodiffeq
Releases · NeuroDiffGym/neurodiffeq
Release of v0.6.3
Happy 2024 🎆 🥳
- Add support for selecting a particular device when there are multiple GPU devices.
- Fix an bug when loading a solver whose
.nets
have shared instance(s) oftorch.nn.Module
. - Miscellaneous documentation improvement.
- PR CI is up and running again
Release of v0.6.2
- Allow update best_net without running validation every epoch
- New 1-D Dirichlet boundary condition for bundles
- Some API changes for bundle solvers and bundle conditions
- Allow saving and loading custom solvers inheriting
Sovler1D
,Solver2D
, orBundleSolver1D
- Suppress error related to removing colorbar caused by newer versions of matplotlib
Release of v0.6.1
Hot fix: solve a fatal compatibility issue with torch v1.13
Release of v0.6.0
- Bug fix: deduplicate parameters when networks share common layers/params
- Removed deprecated APIs
- Forward compatibility with newer versions of PyTorch
Release of V0.5.2
- Better progress bar
- Allow user to conveniently set torch and numpy seeds with
neurodiffeq.utils.set_seed
- Implement solvers.get_residuals
- Misc bug fixes and better error messages
Release of v0.5.1
- Support Chebyshev points of the first and second kine for
Generator1D
,Generator2D
,Generator3D
,GeneratorND
- Modify the usage of
log-spaced
andlog-spaced-noisy
forGenerator1D
to make it consistent with other sampling methods - Support
tqdm
progress bar withsolver.fit()
. Default is writing progress bar to stderr.
Release of v0.5.0
- Integration with NeuroDiffHub (in beta)
- Extensive operator support
- Better monitors
- Clearer documentation
- Miscellaneous improvement
Release of v0.4.0
- Misc bug fixes
- Add Generic Solver (Solver for arbitrary dimensions)
- Custom loss metrics
- More Efficient grad operator
- Bundle Solver for ODE
Release of v0.3.5
Fix an issue where images are not properly rendered on PyPI index page.
Release of v0.3.4
Update the README page & misc changes.