CREATE SAMPLING POINTS FROM LS-OPT¶
Generates a design-of-experiments sampling plan using LS-OPT, producing a table of parameter combinations (experiments) ready for simulation execution. Supports continuous and discrete variables across multiple sampling strategies including D-Optimal, Latin Hypercube, Full Factorial, and Monte Carlo. Use this worker to define the initial or iterative sampling stage of a design-exploration workflow.
When to use¶
Tagged: continuous, d-optimal, design-of-experiments, discrete, doe, full-factorial, latin-hypercube, lsopt.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Parameters | parameters | dataset | (complex) | ✓ | Dataset defining each design variable: name, type (continuous or discrete), default value, min/max bounds, and step range — at least one variable required; discrete variables list allowed values in the ‘min’ field. |
| Sampling Scheme | sampling_scheme | select | dopt | ✓ | DOE strategy used to generate sample points; choose from D-Optimal (default), Space Filling (maximin distance), Full Factorial, Latin Hypercube, Koshal Linear, Composite, or Monte Carlo depending on space coverage and efficiency needs. |
| Points Per Variable | points_per_variable | select | 2 | ✓ | Number of sample points generated per design variable (2–20); higher values increase coverage and resolution but grow the experiment matrix — default is 2; ignored by schemes that compute their own point count (e.g. Full Factorial, Koshal). |
| Number Of Simulations | num_simulations | select | 10 | ✓ | Total number of simulation runs (experiment rows) to generate; selectable from 5 to 50 (default 10); used primarily by space-filling and Monte Carlo schemes where total count drives the sampling density. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| experiments | experiments | dataset | Dataset table where each row is one experiment (simulation run) and each column corresponds to a design variable, populated with the parameter values prescribed by the chosen sampling scheme — feed directly into a solver or job-submission worker. |
Disciplines¶
- design_exploration.doe
- design_exploration.optimization
Auto-generated from platform schema. Worker id: lsopt_create_sampling_points. Schema hash: 4793b56a4f91. Hand-curated docs in workerexamples/ override this page when present.