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name: reduced_recovery_evaluation description: Run bounded reduced Helmholtz PINN recovery with optimizer updates, lambda traces, relative L2 metrics, and validator-compatible evidence.

Reduced Recovery Evaluation

Use this skill for soft-mode recovery when full legacy TensorFlow PINN training is blocked but a mechanism-faithful reduced experiment is allowed. It must execute real parameter updates and record gradient-statistic annealing evidence.

Inputs

• Problem builder and loss modules.
• Trainable model implementation.
• Annealing policy.
• Step count, learning rate, and random seed.
• Output directory for recovery artifacts.

Outputs

• Training trace with params_before, params_after, losses, lambdas, and metrics.
• Recovery result containing numeric relative-L2 metrics.
• Mechanism checks for residual loss, boundary loss, adaptive lambda, and optimizer execution.

Workflow

1. Build a deterministic Helmholtz problem.
2. Initialize a small trainable model.
3. Evaluate relative L2 before training.
4. Compute separated losses and finite-difference gradients.
5. Update lambda from gradient statistics and perform optimizer steps.
6. Evaluate relative L2 after training and write JSON artifacts.

Validation

Run a recovery harness that imports this skill and then run the Distiller recovery experiment validator.