GBOED method

.cache/known_methods_snapshot.json

@@ -125,6 +125,18 @@
         "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"
+      ]
     }
   },
   "observed_methods": [

CLAUDE.md

@@ -61,21 +61,11 @@ calibration_benchmark/
 │   ├── *.nc                     Kalman results (TEKI, ETKI, IEKF)
 │   ├── bayesian/                ABC + HM results
 │   ├── UKI_results/             UKI results
-│   ├── adam_results/            ADAM gradient optimizer results
-│   │                            filenames follow `leaderboard_adam_<benchmark-tag>_<date>.nc`;
-│   │                            benchmark tags: `l63` → L63, `l96_const-force` → L96,
-│   │                            `l96_vec-force` → L96_SPATIAL_FORCING (no L96_NN_FORCING yet)
-│   ├── levenberg_marquardt_results/  LM gradient optimizer results
-│   │                            filenames follow `leaderboard_lm_<benchmark-tag>_<date>.nc`;
-│   │                            same benchmark tags as adam_results/ (no L96_NN_FORCING yet)
+│   ├── adam_results/            ADAM gradient optimizer results (per-benchmark files)
+│   ├── levenberg_marquardt_results/  LM gradient optimizer results (per-benchmark files)
 │   ├── ces-eki-dmc_results/     CES-EKI-DMC ensemble-results files (UQ source)
-│   │                            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)
 │   └── 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,
-│                                `l96_flux-force` → L96_NN_FORCING, `l96_vec-force` → L96_SPATIAL_FORCING
 ├── examples/                    lorenz_demo.ipynb + lorenz.py (reference, not app code)
 ├── From_Rob_6-10/               Raw collaborator data drop — NOT read by the app
 ├── .cache/                      known_methods_snapshot.json (auto-generated, safe to delete)
@@ -191,6 +181,7 @@ 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
 

data/gboed-uq_results/boed_l63_ensemble_results_2026-07-27_minimal.nc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca32e5609af36cfd99cd14ec73ae1f2254f24da96bbf65a16dce023b5a803f76
+size 673057

data/gboed-uq_results/boed_l63_ensemble_results_2026-07-30_minimal.nc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52616c824f068f6de1eb9ccf67f56a1000b56d1cd54c911daa817df4b600e26b
+size 673057

data/gboed-uq_results/boed_l96_const-force_2026-07-28_minimal.nc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50c1939536077245886497fab8b74ffe8382d95b62a55ac7d7ccd66c0815703c
+size 809857

data/gboed-uq_results/boed_l96_const-force_2026-07-30_minimal.nc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84062bc4afda563eba840efcf06c70143037660e99f258d6ecba84538a308b78
+size 809857

data/gboed-uq_results/boed_l96_vec-force_2026-08-06_minimal.nc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8cd67af4f7a347aa156718ba4a759b2ed11a6047813ed6446cef6ed6922df851
+size 536257

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_2026-07-30_minimal.nc"),
     ],
     "L96":                [
         ("ces-eki-dmc", "ces-eki-dmc_results/ces-eki-dmc_l96_ensemble_results_2026-06-15_minimal.nc"),
@@ -100,6 +101,7 @@ UQ_BUDGET_FILES: dict[str, list[tuple[str, str]]] = {
         ("ces-eki-const", "ces-eki-const_results/ces-eki-const_l96_ensemble_results_2026-07-09_minimal.nc"),
         ("ces-iekf-const", "ces-iekf-const_results/ces-iekf-const_l96_ensemble_results_2026-07-17_minimal.nc"),
         ("hm", "history-matching-uq_results/history-matching_l96_const-force_2026-07-20_minimal.nc"),
+        ("gboed", "gboed-uq_results/boed_l96_const-force_2026-07-30_minimal.nc"),
     ],
     "L96_NN_FORCING":     [
         ("ces-eki-dmc", "ces-eki-dmc_results/ces-eki-dmc_l96_nn_forcing_ensemble_results_2026-06-15_minimal.nc"),
@@ -114,6 +116,7 @@ UQ_BUDGET_FILES: dict[str, list[tuple[str, str]]] = {
         ("ces-eki-const", "ces-eki-const_results/ces-eki-const_l96_spatial_forcing_ensemble_results_2026-07-09_minimal.nc"),
         ("ces-iekf-const", "ces-iekf-const_results/ces-iekf-const_l96_spatial_forcing_ensemble_results_2026-07-17_minimal.nc"),
         ("hm", "history-matching-uq_results/history-matching_l96_vec-force_2026-07-22_minimal.nc"),
+        ("gboed", "gboed-uq_results/boed_l96_vec-force_2026-08-06_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)

src/streamlit_app.py

@@ -1,6 +1,17 @@
-import streamlit as st
+import sys
 from pathlib import Path
 
+import pandas as pd
+import streamlit as st
+
+try:
+    from common.method_registry import KNOWN_METHODS
+    from common.leaderboard import _UPDATE_TYPE_ORDER
+except ModuleNotFoundError:
+    sys.path.append(str(Path(__file__).resolve().parent))
+    from common.method_registry import KNOWN_METHODS
+    from common.leaderboard import _UPDATE_TYPE_ORDER
+
 st.set_page_config(page_title="Calibration Benchmark", page_icon="🏠", layout="wide")
 
 # Sidebar navigation
@@ -88,38 +99,51 @@ st.markdown(
     "a surrogate model. See the **📘 Methods** page for citations and per-method "
     "performance charts."
 )
-st.markdown(
-    """
-- **Parallelism** — how the search explores parameter space
-    - **Serial** — `ADAM`, `LM` — a single point estimate advanced step by step.
-    - **Parallel-independent** — `ABC`, `HM` — a population of candidates updated
-      with no coupling between members (accepted samples / per-wave resampling).
-    - **Parallel-interacting** — `TEKI`, `ETKI`, `IEKF`, `UKI`, `CES-EKI-DMC`, `CES-EKI-CONST`,
-      `CES-IEKF-CONST` — an ensemble whose members are coupled through a shared update
-      each iteration.
-- **Update type** — the mechanism driving each update step
-    - **Gradient** — `ADAM`, `LM` — follow the loss gradient (or a Gauss-Newton
-      approximation of it) directly.
-    - **Kalman** — `TEKI`, `ETKI`, `IEKF`, `UKI`, `CES-EKI-DMC`, `CES-EKI-CONST`,
-      `CES-IEKF-CONST` — a (possibly linearized or unscented) Kalman-style ensemble update.
-    - **General** — `ABC`, `HM` — neither gradient- nor Kalman-based (rejection
-      sampling, implausibility cuts).
-- **Method goal** — what the method is built to report
-    - **Optimization** — `TEKI`, `ETKI`, `UKI`, `ADAM`, `LM` — a single best-fit
-      parameter estimate.
-    - **UQ** — `IEKF`, `ABC`, `HM`, `CES-EKI-DMC`, `CES-EKI-CONST`, `CES-IEKF-CONST` — the
-      full posterior / parameter uncertainty. Can still be scored on the Optimization
-      leaderboard, but tends to be less competitive there since it's not optimizing for
-      speed-to-target.
-- **Emulator use** — when/whether a surrogate model of the forward model is used
-    - **None** — `TEKI`, `ETKI`, `IEKF`, `UKI`, `ADAM`, `LM`, `ABC` — samples/evaluates
-      the true forward model throughout.
-    - **Within-optimize** — `HM` — refits a surrogate at each iteration (wave) of the
-      search itself.
-    - **After-optimize** — `CES-EKI-DMC`, `CES-EKI-CONST`, `CES-IEKF-CONST` — fits a
-      surrogate (e.g. a GP) once, after calibration finishes, and samples the posterior
-      through it.
-"""
+_taxonomy_rows = [
+    {
+        "Algorithm": info["abbreviation"],
+        "Method Goal": info["method_goal"],
+        "Update Type": info["update_type"],
+        "Parallelism": info["parallelism"],
+        "Emulator Use": info["emulator_use"],
+        "_update_type_order": _UPDATE_TYPE_ORDER.get(info["update_type"], 99),
+    }
+    for info in KNOWN_METHODS.values()
+]
+_taxonomy_df = (
+    pd.DataFrame(_taxonomy_rows)
+    .sort_values(["_update_type_order", "Algorithm"])
+    .drop(columns="_update_type_order")
+)
+
+_UPDATE_TYPE_ROW_COLOR = {
+    "kalman": "#d9f2e6",    # pastel green
+    "gradient": "#dbe9fa",  # pastel blue
+    "general": "#fde2c8",   # pastel orange
+}
+
+
+def _style_taxonomy_rows(df: pd.DataFrame) -> pd.DataFrame:
+    result = pd.DataFrame("", index=df.index, columns=df.columns)
+    for row in df.index:
+        bg = _UPDATE_TYPE_ROW_COLOR.get(df.loc[row, "Update Type"], "")
+        if bg:
+            result.loc[row, :] = f"background-color: {bg}; color: #212529"
+    return result
+
+
+st.dataframe(
+    _taxonomy_df.style.apply(_style_taxonomy_rows, axis=None),
+    hide_index=True,
+    use_container_width=True,
+    height=int((len(_taxonomy_df) + 1) * 35.2 + 3),
+    column_config={
+        "Algorithm": st.column_config.TextColumn("Algorithm"),
+        "Method Goal": st.column_config.TextColumn("Method Goal"),
+        "Update Type": st.column_config.TextColumn("Update Type"),
+        "Parallelism": st.column_config.TextColumn("Parallelism"),
+        "Emulator Use": st.column_config.TextColumn("Emulator Use"),
+    },
 )
 st.caption(
     "Note: Kalman methods are Bayesian in spirit too (they're approximate Gaussian "