Python tutorial for solving 1D Poisson with libCEED and SciPy CG

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
52/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
numpy, python
Domain
documentation, machine-learning

Research direction

Start by reading Tutorial 5 and the current Python interface to determine whether it supports the proposed right-hand side, boundary conditions, and SciPy CG integration. The issue does not name specific files or tests, so first locate the tutorial and relevant interface examples. Done means a documented 1D Poisson solve with analytical-solution checks and decreasing error under mesh refinement; clarify feasibility and validation expectations with the maintainers first.

Written by the indexing model from the issue text.

Description

examples Python

Hi libCEED team,

I’m Mounika, a PhD student in Computational Science. I’m exploring a contribution to the Python tutorials and would appreciate your guidance on the usefulness and feasibility of this idea.

So, Tutorial 5 demonstrates creating and applying a 1D Poisson operator. I would like to build on that with a worked example showing how to use the operator to solve a complete boundary-value problem.

The proposed tutorial would:

  1. Solve a 1D Poisson problem with a known analytical solution and homogeneous Dirichlet boundary conditions.
  2. Construct the right-hand side and explain how to impose the boundary conditions.
  3. Wrap the libCEED operator in a SciPy LinearOperator and solve using conjugate gradient.
  4. Check the numerical solution against the analytical solution and demonstrate decreasing error under mesh refinement.

Optionally i would like to explore increasing the polynomial degree.

However, before proceeding, I have a few questions about it; could you please advise on the following?

  • Does the current libCEED Python interface expose all the functionality needed to construct the right-hand side, handle the boundary conditions, and connect the operator to SciPy’s CG solver?
  • Would libCEED, NumPy, and SciPy be sufficient for the numerical implementation, or would additional packages or compiled components be required?
  • Is this a recommended approach for a standalone Python tutorial, or would you suggest a different solver integration?
  • Would this be a useful contribution, and is there any existing or ongoing work that I should build on?

If the approach is suitable, I would be happy to start with a minimal example and develop it into a documented tutorial. I would also appreciate guidance on its preferred location and validation requirements.

Thank you!
Mounika

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Forks
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Avg merge
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Merged PRs (30d)
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Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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