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FESurrogateModelTutorial/phases/2-dataset-and-surrogates/step2.md

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+# Step 2: surrogate-models
+
+## 읽어야 할 파일
+
+- `/AGENTS.md`
+- `/docs/theory/02_response_surface_methodology.md`
+- `/docs/theory/03_gaussian_process_kriging.md`
+- `/docs/theory/04_random_forest.md`
+- `/docs/theory/05_gradient_boosting.md`
+- `/docs/theory/06_mlp_neural_network.md`
+- `/src/femsurrogate/surrogates/common.py`
+
+## 작업
+
+TDD로 모델별 scikit-learn pipeline builder와 registry를 구현한다.
+
+- 테스트: `/tests/test_surrogate_models.py`.
+- 구현: `rsm.py`, `gpr.py`, `random_forest.py`, `boosting.py`, `mlp.py`, `registry.py`.
+- 모델명: `rsm`, `gpr`, `random_forest`, `gradient_boosting`, `mlp`.
+
+## Acceptance Criteria
+
+```powershell
+uv run pytest tests/test_surrogate_models.py -q
+uv run ruff check .
+```
+
+## 금지사항
+
+- PyTorch/TensorFlow, AutoML, MLflow를 추가하지 마라.