initial commit FESurrogateModelTutorial

FESurrogateModelTutorial/phases/2-dataset-and-surrogates/index.json

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+{
+  "project": "FEMSurrogateTutorial",
+  "phase": "2-dataset-and-surrogates",
+  "steps": [
+    {
+      "step": 0,
+      "name": "sampling-and-dataset",
+      "status": "completed",
+      "summary": "Added reproducible LHS sampling, BeamParameters/AnalysisResult schema, and Beam2D dataset builder using the in-repository solver."
+    },
+    {
+      "step": 1,
+      "name": "surrogate-common",
+      "status": "completed",
+      "summary": "Added reproducible dataset split helper, evaluation result dataclasses, regression metrics, fit/predict timing, and prediction table generation."
+    },
+    {
+      "step": 2,
+      "name": "surrogate-models",
+      "status": "completed",
+      "summary": "Added scikit-learn builders and registry for RSM, GPR, Random Forest, Gradient Boosting, and MLP models."
+    },
+    {
+      "step": 3,
+      "name": "plotting-and-results",
+      "status": "completed",
+      "summary": "Added parity and residual diagnostic plots, metrics table creation, and metric comparison plot helpers returning matplotlib figures."
+    }
+  ]
+}

FESurrogateModelTutorial/phases/2-dataset-and-surrogates/step0.md

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+# Step 0: sampling-and-dataset
+
+## 읽어야 할 파일
+
+- `/AGENTS.md`
+- `/docs/ARCHITECTURE.md`
+- `/docs/theory/01_doe_sampling_validation.md`
+- `/src/femsurrogate/fea/`
+
+## 작업
+
+TDD로 LHS sampling과 Beam2D dataset builder를 구현한다.
+
+- 테스트: `/tests/test_sampling.py`, `/tests/test_dataset_builder.py`.
+- 구현: `/src/femsurrogate/data/bounds.py`, `schema.py`, `sampling.py`, `dataset.py`.
+- 기본 seed는 `20260521`.
+- dataset schema는 문서의 SI 단위 컬럼을 따른다.
+
+## Acceptance Criteria
+
+```powershell
+uv run pytest tests/test_sampling.py tests/test_dataset_builder.py -q
+uv run ruff check .
+```
+
+## 금지사항
+
+- surrogate 학습을 이 step에 넣지 마라.

FESurrogateModelTutorial/phases/2-dataset-and-surrogates/step1.md

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+# Step 1: surrogate-common
+
+## 읽어야 할 파일
+
+- `/AGENTS.md`
+- `/docs/ARCHITECTURE.md`
+- `/src/femsurrogate/data/`
+
+## 작업
+
+TDD로 surrogate 공통 split/evaluate/result helper를 구현한다.
+
+- 테스트: `/tests/test_surrogate_common.py`.
+- 구현: `/src/femsurrogate/surrogates/common.py`.
+- metrics는 `rmse`, `mae`, `r2`, `fit_time_s`, `predict_time_s`를 포함한다.
+
+## Acceptance Criteria
+
+```powershell
+uv run pytest tests/test_surrogate_common.py -q
+uv run ruff check .
+```
+
+## 금지사항
+
+- 개별 모델 builder를 이 step에 섞지 마라.

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.

FESurrogateModelTutorial/phases/2-dataset-and-surrogates/step3.md

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+# Step 3: plotting-and-results
+
+## 읽어야 할 파일
+
+- `/AGENTS.md`
+- `/docs/ARCHITECTURE.md`
+- `/src/femsurrogate/surrogates/`
+
+## 작업
+
+TDD로 diagnostics plotting과 result comparison helper를 구현한다.
+
+- 테스트: `/tests/test_plotting_and_results.py`.
+- 구현: `/src/femsurrogate/plotting/diagnostics.py`, `/src/femsurrogate/plotting/comparison.py`.
+- plotting 함수는 matplotlib Figure를 반환한다.
+
+## Acceptance Criteria
+
+```powershell
+uv run pytest tests/test_plotting_and_results.py -q
+uv run ruff check .
+```
+
+## 금지사항
+
+- browser dashboard를 만들지 마라.