.. _auto_lsopt_create_sampling_points:

*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
------

.. list-table::
   :header-rows: 1
   :widths: 20 20 20 20 20 20

   * - 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
-------

.. list-table::
   :header-rows: 1
   :widths: 20 20 20 20

   * - 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

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   Auto-generated from <code>platform</code> schema. Worker id: <code>lsopt_create_sampling_points</code>. Schema hash: <code>4793b56a4f91</code>. Hand-curated docs in <code>workerexamples/</code> override this page when present.
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