Simulation to learning¶
AgentFEM does not replace PyTorch or a user's neural-network stack. It makes the path from a parameterized finite-element model to accepted, traceable training data much shorter and more systematic.
Workflow¶
parameter space → reproducible FEM campaign → result policy
→ accepted ScientificDataset → NumPy/PyTorch adapter
→ built-in or user model → validation/applicability guard
→ learned prediction or FEM fallback
Available building blocks¶
- parameter campaigns with persistent case identity and parallel execution;
- dataset acceptance rules rather than silent collection of failed cases;
- NumPy and optional PyTorch adapters;
- ridge and POD-ridge baselines plus an optional PyTorch MLP;
- user-owned PyTorch estimators without inheritance from a proprietary model class;
- training/validation separation, applicability checks, and high-fidelity FEM fallback.
This observation and dataset contract is the basis for future neural operators and digital-twin state updating. Those directions require stable field encodings, sensor identity, time alignment, and uncertainty—not just another neural-network class.