tensorflow / tensorflow/probability
How to subclass Solver to create an Euler method
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.4k
- Forks
- 1.1k
- PR merge metrics
- No merged PRs in 30d
Description
Hello, I am trying to subclass Solver to have an Euler explicit method in order to enjoy the adjoint sensitivity method for the computation of the gradient together with all the TensorFlow machinery. It is honestly just too nice. However, I am finding myself literally deleting and re-adding DormandPrince line after line, that is a bloodbath.
I could not find any tutorial about custom solvers in the documentation, I was wondering if there is something I can do to tweak the available solvers or if there is an efficient way to subclass Solver.
Thank you.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue mentions Solver, the DormandPrince solver, Euler's method, and TensorFlow's adjoint sensitivity machinery, but no files or tests. Start by reviewing the available solver documentation and the Solver entry points; done should be clear guidance or a tutorial showing how to customize or subclass a solver.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- documentation
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100