SAMPLING DOMAIN REDUCER¶
Narrows the sampling domain around a known optimum by shrinking variable bounds according to a chosen reduction strategy (distance-based, direct scaling, or iso-parametric). Optionally drops low-sensitivity variables to constants using feature-importance rankings, accelerating convergence in sequential DOE workflows.
When to use¶
Tagged: doe, domain reduction, feature importance, optimization loop, sampling, sequential, variable bounds.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Current Variables | current_variables | dataset | — | ✓ | Dataset describing the current design variables including their names, types, lower/upper bounds, and any constant flags — serves as the sampling domain to be reduced. |
| Current Optimum | current_optimum | dataset | — | ✓ | Dataset containing the current best (optimum) variable values around which the new, tighter bounds will be centered. |
| Percentage Reduction | percentage_reduction | scalar | 80 | ✓ | Fraction by which to shrink the variable bounds around the optimum; accepts a percentage (e.g., 80 means 80 %) or a ratio (e.g., 0.8) — defaults to 80. |
| Variable Names | variable_names | text | — | Optional comma-separated list of variable names to restrict reduction to a specific subset; leave blank to apply reduction to all variables. | |
| Reduction Type | reduction_type | select | distance_from_min_max_max | Strategy used to compute the new bounds: ‘distance_from_min_max_max’ shrinks the gap between optimum and original min/max (default); ‘scale_optimum_value’ scales the optimum value directly; ‘scale_isoparametric’ uses iso-parametric position; ‘scale_optimum_value_within_minmax’ scales while clamping to original limits. | |
| Feature Importance | feature_importance | dataset | — | Optional dataset of per-variable sensitivity/importance scores; when provided, variables below the importance threshold are frozen to constants rather than being sampled. | |
| Feature Importance Threshold | feature_importance_drop_threshold | scalar | 0.8 | Percentile/score cutoff (0–1 scale, default 0.8) below which a variable’s sensitivity is considered negligible and the variable is converted to a constant. | |
| Convert Boundary To Constant Type | expansion | select | no | Controls behaviour when the optimum sits on a bound: ‘no’ leaves it unchanged (default), ‘constant’ converts the boundary variable to a constant, or ‘expand’ pushes the bound outward. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| New Variables | dataset | dataset | Updated variables dataset with reduced bounds centered around the current optimum, ready for use as the sampling domain in the next sequential DOE iteration. |
Disciplines¶
- ai_ml.prognosis
- design_exploration.doe
- design_exploration.optimization
- design_exploration.sensitivity
Auto-generated from platform schema. Worker id: doe_sampling_sequential_reduction. Schema hash: 99b1a582442c. Hand-curated docs in workerexamples/ override this page when present.