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---
name: gated_pinn_architecture
description: Build a lightweight gated PINN-style scalar model that exposes trainable parameters and gradient paths for reduced recovery experiments.
---
# Gated PINN Architecture
Use this skill when a PINN recovery wants to exercise the paper's improved architecture idea or compare it with a plain parameterization. The standard-library implementation is intentionally small for bounded recovery; full experiments may replace it with a neural-network library implementation.
## Inputs
- Basis feature count or hidden width.
- Initialization seed.
- Whether to use gated feature mixing.
## Outputs
- Trainable model parameters.
- Forward predictions for coordinate points.
- Parameter count and serializable state.
## Workflow
1. Create sinusoidal Helmholtz-like basis functions.
2. Add trainable weights and optional gate scalars that mix coordinate transforms.
3. Expose prediction and finite-difference/free analytic derivatives needed by the loss module.
4. Return parameter snapshots before and after optimization.
## Validation
Run `python scripts/gated_model.py --self-test` or the skill-tree tests.
