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생산성 및 협업 · SKILL.md

curriculum_regularization

curriculum_regularization

Generate coefficient schedules for curriculum-regularized PINN recovery experiments.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: curriculum_regularization description: Generate coefficient schedules for curriculum-regularized PINN recovery experiments.

Curriculum Regularization

Use this skill when a PINN failure-mode experiment should begin with an easier PDE residual and progress to the target coefficient. Do not use it to silently change the final target coefficient.

Inputs

  • Starting coefficient, target coefficient, number of stages, and residual weight.
  • Optional policy constraints for monotonicity and endpoint matching.

Outputs

  • A list of stages with beta and residual_weight fields.
  • Validation metadata covering stage count, monotonicity, and target endpoint.

Workflow

  1. Choose an easy starting coefficient based on the reduced experiment.
  2. Generate a deterministic monotone schedule up to the target coefficient.
  3. Preserve residual weight as a separate field from the PDE coefficient.
  4. Pass the schedule to the recovery harness one stage at a time.
  5. Save the schedule in traces so analysis can verify curriculum execution.

Validation

Run python scripts/schedule.py --start 1 --target 30 --stages 5 and the tests in tests/.

Limitations

The included scheduler is linear and coefficient-focused. More complex curricula may add adaptive stages, but they must keep explicit endpoint and monotonicity checks.

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