AI-assisted finite-element simulation¶
You want to know how a component deforms under load. An AI assistant can help prepare the model and organize the work, but the answer still needs a stated geometry, material law, load, constraint, and numerical solve.
AgentFEM connects those two sides: an agent operates the workflow; the FEniCSx-based numerical runtime evaluates the declared model. You can read and edit the same Python project without using an AI service.
Start with one cantilever¶
Install a compatible runtime using the installation guide, activate that environment, and check it:
In a new, empty project directory, run:
agentfem init --template static-solid .
agentfem check
agentfem run --name baseline
agentfem show latest
agentfem verify
The template supplies a small linear-elastic structural example. Read its
case.py before changing it: that file is the model, not a hidden prompt or a
set of settings held by a chat application. The generated project README and
solid-mechanics guide explain the modeling
conventions.
Ask the agent for a bounded task¶
Connect the official adapter using the MCP installation guide. It exposes seven lifecycle tools, including project creation, checks, run submission, and result inspection. The adapter requires the numerical runtime above; it does not install that runtime for you.
Use a request with an observable outcome:
Create a new AgentFEM static-solid project inside the approved project directory. Explain its dimensions, material, fixed boundary, load, and units before running it. Check the model, run the baseline, and show the displacement result together with the result file and convergence status. Do not replace existing projects.
The useful outcome is not just an answer in chat. It is a project you can open, rerun, change, and share.
What to look at after the solve¶
| Question | Where to look |
|---|---|
| What did we actually model? | case.py and named regions |
| Did the numerical process finish? | The run's execution record |
| What displacement and stress were computed? | result.json and its field artifacts |
| What checks support this result? | Convergence and verification evidence |
| How do I see the field? | The recommended visualization artifact; see results |
Make one controlled comparison¶
For an unchanged, small-displacement linear-elastic model, doubling the applied load should double displacement and stress. Ask the agent to prepare a second case, preserve the baseline, and compare the same output definition in both. This is a useful consistency check, not a substitute for mesh convergence or experimental validation.
That workflow illustrates the role of AI here: less repetitive setup and navigation, while the equations, assumptions, and numerical evidence remain available to the engineer.