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

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

uv run pytest tests/test_surrogate_models.py -q
uv run ruff check .

금지사항

• PyTorch/TensorFlow, AutoML, MLflow를 추가하지 마라.