framework for gboed

CLAUDE.md

@@ -72,6 +72,8 @@ calibration_benchmark/
 │   │                            filenames follow `ces-eki-dmc_<benchmark-tag>_ensemble_results_<date>[_minimal].nc`;
 │   │                            benchmark tags: `l63` → L63, `l96` (no suffix) → L96,
 │   │                            `l96_nn_forcing` → L96_NN_FORCING, `l96_spatial_forcing` → L96_SPATIAL_FORCING
+│   ├── gboed-uq_results/        GBOED ensemble-results files (UQ source; L63 only so far)
+│   │                            filenames follow `boed_<benchmark-tag>_ensemble_results_<date>[_minimal].nc`
 │   └── gnki-uq_results/         IEKF (a.k.a GNKI) ensemble-results files (UQ source)
 │                                filenames follow `leaderboard_gnki_<benchmark-tag>_<date>[_minimal].nc`;
 │                                benchmark tags: `l63` → L63, `l96_const-force` → L96,
@@ -191,6 +193,7 @@ A quick taxonomy tree is rendered on the home page (`src/streamlit_app.py`).
 | `ces-eki-dmc` | CES-EKI-DMC | parallel-interacting | kalman | uq | after-optimize | — |
 | `adam` | ADAM | serial | gradient | optimization | none | — |
 | `lm` | LM | serial | gradient | optimization | none | gradient_descent |
+| `gboed` | GBOED | parallel-interacting | general | uq | within-optimize | boed |
 
 ## How to extend
 

src/common/method_registry.py

@@ -134,6 +134,15 @@ KNOWN_METHODS = {
         "emulator_use": "none",
         "aliases": ["lm", "levenberg_marquardt", "levenberg-marquardt", "gradient_descent"],
     },
+    "gboed": {
+        "abbreviation": "GBOED",
+        "Method": "Goal-oriented Bayesian Optimal Experimental Design",
+        "parallelism": "parallel-interacting",
+        "update_type": "general",
+        "method_goal": "uq",
+        "emulator_use": "within-optimize",
+        "aliases": ["gboed", "boed"],
+    },
 }
 
 
@@ -142,6 +151,7 @@ KNOWN_METHODS = {
 _METHOD_PALETTE = [
     "#4c78a8", "#f58518", "#e45756", "#72b7b2", "#54a24b",
     "#eeca3b", "#b279a2", "#ff9da6", "#9d755d", "#bab0ac",
+    "#5254a3", "#8ca252",
 ]
 
 # Stable abbreviation → hex color mapping.  Import this wherever Altair charts are built

src/data_store.py

@@ -93,6 +93,7 @@ UQ_BUDGET_FILES: dict[str, list[tuple[str, str]]] = {
         ("ces-eki-const", "ces-eki-const_results/ces-eki-const_l63_ensemble_results_2026-07-09_minimal.nc"),
         ("ces-iekf-const", "ces-iekf-const_results/ces-iekf-const_l63_ensemble_results_2026-07-17_minimal.nc"),
         ("hm", "history-matching-uq_results/history-matching_l63_ensemble_results_2026-07-20_minimal.nc"),
+        ("gboed", "gboed-uq_results/boed_l63_ensemble_results_2026-07-27_minimal.nc"),
     ],
     "L96":                [
         ("ces-eki-dmc", "ces-eki-dmc_results/ces-eki-dmc_l96_ensemble_results_2026-06-15_minimal.nc"),

src/pages/MethodDetails.py

@@ -94,6 +94,11 @@ method_meta = {
         "url": "https://doi.org/10.1090/qam/10666",
         "summary": "Levenberg-Marquardt — damped least-squares algorithm that interpolates between gradient descent and Gauss-Newton steps for efficient nonlinear least-squares minimization.",
     },
+    "GBOED": {
+        "citation": "Holthuijzen et al. 2026",
+        "url": "https://arxiv.org/abs/2508.13071",
+        "summary": "Goal-oriented Bayesian Optimal Experimental Design — selects each batch of forward-model evaluations by maximizing an expected-information-gain criterion targeted at the quantity of interest, refitting the surrogate used by the criterion at every iteration.",
+    },
 }
 
 # Selection UI (defaults to query param if valid)