QuIP: Experimental Design for Expensive Simulators with Qualitative Factors

A 5.4 MB dataset by Yen-Chun Liu, last updated May 8, 2026, describing the QuIP framework for designing experiments with expensive simulators and many qualitative factors. The work focuses on path planning and rover trajectory optimization, using Gaussian process surrogates and integer programming for efficient design selection. Files are provided in PDF, ZIP, and TXT formats.

Use Cases

• Optimizing path planning parameters based on the described Gaussian process surrogate framework.
• Designing experiments for expensive simulators with qualitative factors using the integer programming method.
• Benchmarking sequential design strategies for black-box optimization tasks mentioned in the description.
• Applying the assignment problem formulation to initial design selection in high-dimensional discrete spaces.

Strengths

• Dataset is 5.4 MB, making it a small and manageable download.
• Released under the permissive CC-BY-4.0 license.
• Includes a detailed description of a novel methodological framework (QuIP).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Likely contains results from simulation experiments and methodological descriptions.

Time Range

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Freshness

Last updated 2026-05-08 16:31:30; freshness should be verified.

Geography

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