Parametric optimisation study
Open
Nobody has claimed this yet.
backend
enhancement
frontend
functionality
- Dominant language
- TypeScript
- Stars
- 91
- Forks
- 14
- Avg merge
- 4m
- Merged PRs (30d)
- 7
Description
Instead of a "deterministic" simulation, users should be able to set parameters as free parameters and run parametric optimisation studies based on a set of constraints and goals.
Examples:
- In the ARC fuel cycle simulation, minimise the startup storage inventory while ensuring the total energy produced in the first year is greater than X.
- Find the mass transfer coefficients that fit the tritium release curve that was experimentally measured (see this tutorial)
UI needs:
- Set a parameter as free parameter: similar to #181 users would be able to set a parameter as "free"
- Give bounds to a parameter (eg. parameter A cannot be negative)
- Define a cost function/error python function
def error(p): ... return e - Select an optimiser (from
scipy.optimisemethods?)
Contributor guide
No contributing guide indexed for this repository
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
Start by comparing the requested free-parameter workflow with issue #181 and review the linked tritium-release tutorial. Investigate the available scipy.optimize methods and how the UI currently exposes parameters. Done would mean a defined approach for bounds, a Python cost function, optimizer selection, and the listed optimisation studies.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, typescript
- Domain
- backend, frontend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100