Implement localization/regularization that is built-in to the `fit` procedure of ensemble based assimilation
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 161
- Forks
- 141
- Avg merge
- 2d 4h
- Merged PRs (30d)
- 138
Description
Localization is a type of (usually informed, on position in time and space) regularization.
It is well known that regularization works best when directly built-in to the objective function of the fit, both when H is known and must be learnt.
This is in contrast to adaptive localization and distance based localization.
Likely, a better approach is to
- #5633
- #5634
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 names no files, tests, or entry points. Start by reviewing the fit procedure and the linked issues #5633 and #5634 to establish the intended localization or regularization approach. The issue does not define acceptance criteria, so completion would require a decided design and explicit tests or behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100