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Creating Codebase Docs ​

Creating codebase documentation is an optional step, but it becomes essential when you are developing a new solver or expanding an existing computational framework. In computational engineering projects — where codes involve multiple physics modules, numerical solvers, and data post-processing — clear documentation ensures that your work remains understandable, reproducible, and scalable for future researchers.

Why It Matters ​

Solver codebases in computational engineering often evolve over months or years, with contributions from multiple team members. Without proper documentation, the logic behind boundary conditions, numerical schemes, or mesh-handling routines can become difficult to trace. A well-structured codebase doc serves as a technical map — allowing others (and your future self) to navigate the code confidently.

What to Include ​

If you are building a new solver or significantly modifying one, maintain a README.md or a /docs folder at the root of your repository. Include the following key sections:

1. Overview ​

Describe the objective of the solver, the physical problem it addresses (e.g., EM wave propagation, heat transfer, structural vibration), and the governing equations or formulations it implements.

2. Code Structure ​

Outline the folder hierarchy and purpose of each module. For example:

/src          → solver source files  
/modules      → physics modules (e.g., EM, thermal, structural)  
/meshes       → input geometry and mesh files  
/results      → output or post-processing data  
/utils        → helper scripts and functions

3. Workflow and Usage ​

Provide clear instructions on how to run the solver from start to finish. Include:

  • Prerequisites and dependencies (Python/FEniCS versions, libraries, etc.)
  • Example commands or shell scripts for running simulations
  • Typical input/output file formats

For example:

python3 main_em_solver.py --mesh antenna.xdmf --freq 9e9

4. Numerical Details ​

Briefly describe key numerical aspects such as:

  • Element type used (e.g., Nedelec, Lagrange, DG)
  • Discretization scheme (explicit/implicit, Crank–Nicolson, etc.)
  • Boundary condition implementation
  • Stability or convergence criteria

5. Post-Processing and Visualization ​

Mention how to visualize results (e.g., using ParaView or matplotlib) and how output data (fields, stresses, temperatures, etc.) is stored.

6. References and Validation ​

If the solver is based on published formulations or validated against benchmark problems, include references or comparison plots. This adds credibility and helps others verify accuracy.

Summary ​

While optional for smaller scripts, creating codebase documentation is vital for any solver or research-grade computational tool. It transforms complex code into a readable, teachable, and extensible resource. A well-documented computational solver not only accelerates collaboration within Avkalan Labs but also ensures that your contributions remain usable and citable long after the project concludes.