Calibration Twin: Dupuit Fixed-Head 1D#
Note
This page and its static assets are auto-generated by python -m tools.doc_gallery. The Sphinx build only reads committed PNG and JSON artifacts.
Same-solver twin benchmark on dupuit_fixed_head_1d with one scalar K value and one outlet-discharge observable.
success_metric=best_fit
meets_target=True
truth_recovered=True
cost=0
n_eval=11
calibration=3.23 s
candidate runtime=2.968 s
algorithm overhead=0.2621 s
actualize=0 s
launcher prep=0 s
runtime patch=0 s
simulate=0.2698 s
output select=0 s
objective score=0 s
success_metric=best_fit
meets_target=True
truth_recovered=True
cost=0.0382717
n_eval=24
calibration=7.583 s
candidate runtime=6.841 s
algorithm overhead=0.742 s
actualize=0 s
launcher prep=0 s
runtime patch=0 s
simulate=0.285 s
output select=0 s
objective score=0 s
success_metric=best_fit_or_distribution
meets_target=True
truth_recovered=True
cost=0.012161
n_eval=32
calibration=11.14 s
candidate runtime=9.969 s
algorithm overhead=1.174 s
actualize=0 s
launcher prep=0 s
runtime patch=0 s
simulate=0.3115 s
output select=0 s
objective score=0 s
success_metric=best_fit
meets_target=True
truth_recovered=True
cost=0.0116379
n_eval=28
calibration=11.4 s
candidate runtime=10.19 s
algorithm overhead=1.213 s
actualize=0 s
launcher prep=0 s
runtime patch=0 s
simulate=0.3639 s
output select=0 s
objective score=0 s
success_metric=best_fit
meets_target=True
truth_recovered=True
cost=0
n_eval=30
calibration=9.899 s
candidate runtime=8.986 s
algorithm overhead=0.9125 s
actualize=0 s
launcher prep=0 s
runtime patch=0 s
simulate=0.2995 s
output select=0 s
objective score=0 s
success_metric=best_fit
meets_target=True
truth_recovered=True
cost=0
n_eval=18
calibration=4.021 s
candidate runtime=3.642 s
algorithm overhead=0.3793 s
actualize=0 s
launcher prep=0 s
runtime patch=0 s
simulate=0.2023 s
output select=0 s
objective score=0 s
Case Setup#
Solver: modflow6 in steady regime.
Benchmark family: Supplementary Scalar Reference Cases.
Truth parameters: K_global.
Observed outputs: q_east.
Benchmarked methods: grid, random_search, optuna, cma_es, scipy_nelder_mead, scipy_nelder_mead.
Initial bounds widened to: K_global=[5e-05, 0.0003].
What It Shows#
A same-solver twin experiment where synthetic observations are generated first, then recovered through calibration on the same physics stack.
A case-level configuration figure, an objective trace, and an objective landscape or pairwise projection for the selected display method.
Per-method timing diagnostics with total calibration time plus average per-model preparation, simulation, and objective-evaluation costs.
How To Read It#
Open case_configuration.png first to understand the parameter block, outputs, and weighting before reading the optimization figures.
Use objective_trace to judge convergence speed and objective_landscape to see where the evaluated candidates concentrate relative to the truth and the selected solution(s).
Read timing metrics as benchmark diagnostics, not as universal solver performance numbers: they depend on the chosen method, case size, and evaluation budget.
Key Metrics#
Methods: 5
Display method: random_search
Calibration total: 28.41 s
Session prep: 29.98 s
Candidate runtime: 28.05 s
Algorithm overhead: 0.3577 s
Model total: 1.169 s
Actualize: 0.01682 s
Launcher prep: 0.01682 s
Runtime patch: 0 s
Model prep: 0.01682 s
Model sim: 1.152 s
Output select: 0 s
Objective score: 0 s
Next Steps#
Compare this case with the other calibration gallery pages to see how deterministic and distribution-valued methods behave under different inverse problems.
Use the full benchmark suite in validation_cases/calibration when you need multi-seed comparisons or noisy variants beyond the curated gallery subset.
Reproduce#
Run the underlying example or validation case with:
python -m validation_cases.calibration.twin.steady.dupuit_fixed_head_1d.run_case --case standard
Refresh the committed gallery artifacts with:
python -m tools.doc_gallery
Case Parameters#
Benchmark Setup#
Field |
Meaning |
Value |
Source |
|---|---|---|---|
|
Solver family used both to generate synthetic observations and to calibrate candidates. |
modflow6 |
|
|
Flow regime exercised by the inverse benchmark. |
steady |
|
|
Observables extracted from each candidate simulation and used in the composite objective. |
q_east |
|
|
Synthetic noise injected after the truth run, if any. |
none |
|
Calibrated Parameters#
Field |
Meaning |
Value |
Source |
|---|---|---|---|
|
Truth value, initial search interval, and acceptance tolerance for this calibrated parameter. |
truth=0.0001, bounds=5e-05, 0.0003, tolerance=2e-05 |
|
Methods And Timing#
Field |
Meaning |
Value |
Source |
|---|---|---|---|
|
Method result summary including target status, evaluation count, total time, and mean per-model actualize / launcher / simulation / objective timings. |
meets_target=true, cost=0, n_eval=11, calib_s=11.223, candidate_runtime_s=11.1009, algorithm_overhead_s=0.122142, actualize_s=0.0156029, launcher_prep_s=0.0156029, runtime_patch_s=0, model_sim_s=0.993566, output_select_s=0, objective_score_s=0 |
|
|
Method result summary including target status, evaluation count, total time, and mean per-model actualize / launcher / simulation / objective timings. |
meets_target=true, cost=0.0382717, n_eval=24, calib_s=28.4106, candidate_runtime_s=28.0529, algorithm_overhead_s=0.357668, actualize_s=0.0168222, launcher_prep_s=0.0168222, runtime_patch_s=0, model_sim_s=1.15205, output_select_s=0, objective_score_s=0 |
|
|
Method result summary including target status, evaluation count, total time, and mean per-model actualize / launcher / simulation / objective timings. |
meets_target=true, cost=0.0116379, n_eval=28, calib_s=26.6515, candidate_runtime_s=26.2882, algorithm_overhead_s=0.363368, actualize_s=0.0140536, launcher_prep_s=0.0140536, runtime_patch_s=0, model_sim_s=0.924809, output_select_s=0, objective_score_s=0 |
|
|
Method result summary including target status, evaluation count, total time, and mean per-model actualize / launcher / simulation / objective timings. |
meets_target=true, cost=0, n_eval=30, calib_s=24.2252, candidate_runtime_s=23.9076, algorithm_overhead_s=0.31756, actualize_s=0.0148355, launcher_prep_s=0.0148355, runtime_patch_s=0, model_sim_s=0.782085, output_select_s=0, objective_score_s=0 |
|
|
Method result summary including target status, evaluation count, total time, and mean per-model actualize / launcher / simulation / objective timings. |
meets_target=true, cost=0, n_eval=18, calib_s=16.4948, candidate_runtime_s=16.2896, algorithm_overhead_s=0.205264, actualize_s=0.0154713, launcher_prep_s=0.0154713, runtime_patch_s=0, model_sim_s=0.889505, output_select_s=0, objective_score_s=0 |
|
Displayed Metrics#
Field |
Meaning |
Value |
Source |
|---|---|---|---|
|
Metric surfaced on the gallery page for the selected display method. |
5 |
|
|
Metric surfaced on the gallery page for the selected display method. |
random_search |
|
|
Metric surfaced on the gallery page for the selected display method. |
28.41 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
29.98 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
28.05 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
0.3577 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
1.169 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
0.01682 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
0.01682 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
0 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
0.01682 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
1.152 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
0 s |
|
|
Metric surfaced on the gallery page for the selected display method. |
0 s |
|
Source Pointers#
validation_cases/calibration/README.mdvalidation_cases/calibration/run_benchmarks.pyvalidation_cases/calibration/plotting.pyvalidation_cases/calibration/shared/definitions.pyvalidation_cases/calibration/shared/runtime.pyvalidation_cases/calibration/twin/steady/dupuit_fixed_head_1d/run_case.pyvalidation_cases/calibration/twin/steady/dupuit_fixed_head_1d/experiment.pyhydromodpy/calibration/cli.pyhydromodpy/calibration/engine.py
Artifacts#
docs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__configuration.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__grid_search_landscape.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__grid_search_trace.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__random_search_landscape.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__random_search_trace.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__cma_es_landscape.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__cma_es_trace.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__simplex_landscape.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__simplex_trace.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__nelder_mead_landscape.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6__nelder_mead_trace.pngdocs/readthedocs/source/_static/capability_gallery/calibration/calibration_twin_dupuit_fixed_head_modflow6_summary.jsonstores the displayed metrics plus source hashes used bypython -m tools.doc_gallery --check.