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Model Library

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The Model Library is a registry of all computational models available in your workspace — physics models, ML models, kinetic models, domain-specific solvers, and custom scripts. Models are registered once, reusable across any project, fully versioned, and traceable.

On This Page

Why It Exists

Without a shared model library, teams rebuild the same models for each project — wasting time and introducing inconsistencies. Protos makes models first-class reusable assets:

The Co-engineer can register models for you. Upload a Python script or point it at a GitHub repo and ask it to register the model — it will infer the input/output schema automatically.

  • Register once, call from any project
  • Every simulation run references the exact model version it used
  • Results remain reproducible indefinitely
  • All inputs, outputs, and provenance are traceable

Registering a Model

  1. Go to Model Library in the sidebar.
  2. Click Register Model.
  3. Fill in the required fields:
Field Description
Name Clear, searchable name (e.g. Doyle-Fuller-Newman Electrochemical Model)
Description What the model does, when to use it, known limitations
Domain e.g. electrochemistry, thermal, mechanical, materials
Inputs Each input: name, type, unit, required or optional
Outputs Each output: name, type, unit
Source Python script, COMSOL file, external API endpoint, etc.
Version tag e.g. v1.0.0 — use semantic versioning
  1. Click Save. The model is now available in Simulation Studio.

Tip: Document inputs and outputs fully — include units, valid ranges, and edge case notes. Future users (including you, six months from now) will thank you.

Model Source Types

Source type How it works in Protos
Python script Upload the script; Protos executes it in a managed environment
COMSOL / MATLAB file Upload the model file; Protos calls it with the specified input parameters
External API Provide the endpoint URL and auth config; Protos calls it on run
GitHub repo Point to a public GitHub repo; Protos clones it, generates a wrapper, and containerises it

Finding a Model

  • Search by name, domain, or keyword.
  • Filter by domain, input type, or output type.
  • Sort by most recently used or most recently updated.
  • Click any model to see its full documentation, input/output schema, and version history.

Model Versioning

Every update to a model creates a new version. This is critical for reproducibility.

What versioning gives you Detail
Reproducibility Old simulation runs always reference the exact model version they used — results never change retroactively
Comparison Old canvas runs always reference the exact model version they used, so you can see how results changed between versions
Audit trail Full history of who changed what and when

To update a model:

  1. Open the model in the Model Library.
  2. Click New Version.
  3. Upload the updated source and revise the description.
  4. Add a changelog note describing what changed.

Warning: Do not delete old model versions. Even if a model is superseded, old versions are needed to reproduce past simulation runs.

Best Practices

  • Use semantic versioning: v1.0.0 → v1.1.0 for non-breaking updates, v2.0.0 for breaking changes to inputs or outputs.
  • Tag models by domain — makes them far easier to find in Simulation Studio.
  • Note known limitations in the description — an honest description of edge cases prevents misuse and prevents future users from discovering limitations the hard way.
  • Don't rebuild models already in the library — search before registering. If a close match exists, consider extending it with a new version instead.

See Also

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