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---
name: pinn_loss_decomposition
description: Compute separated Helmholtz PINN residual and boundary losses so gradient imbalance can be diagnosed and corrected.
---
# PINN Loss Decomposition
Use this skill when recovery needs separate PDE residual and boundary/data-fit loss terms. Do not collapse losses before gradient-statistic annealing.
## Inputs
- A trainable model with coordinate predictions.
- Interior collocation points and forcing values.
- Boundary points and exact boundary values.
- Helmholtz parameters and current lambda values.
## Outputs
- Residual loss.
- Boundary loss.
- Weighted total loss.
- Per-parameter gradients for residual and boundary losses when a compatible reduced model is used.
## Workflow
1. Predict the solution on interior and boundary samples.
2. Compute the Helmholtz residual or a reduced residual proxy for the selected model family.
3. Compute mean-squared residual and boundary errors separately.
4. Combine them with current lambda weights only after logging the individual terms.
## Validation
Run `python scripts/losses.py --self-test` or the skill tests.
