Optimal Experimental Design Data for Multiresponse Models via Semidefinite Programming

A 233.4 KB dataset authored by Belmiro P.M. Duarte and last updated on 2026-06-02. It supports a framework for maximum von Neumann entropy optimal experimental design, leveraging semidefinite programming for multiresponse models with correlated outputs. The dataset includes Python code and a PDF document illustrating applications in dose-response, sensor placement, and kinetic modeling.

Use Cases

• Designing dose-response experiments for joint efficacy–toxicity modeling based on the described framework.
• Optimizing sensor placement in multimodal systems using the von Neumann entropy criterion for balanced information allocation.
• Estimating parameters in nonlinear kinetic models via the proposed convex formulation for efficient computation.
• Assessing design optimality using the accompanying equivalence theorem mentioned in the description.

Strengths

• Includes both Python code (PY) and explanatory documentation (PDF) for implementation.
• The methodology is grounded in a tractable convex formulation enabling efficient computation via interior-point methods.
• Framework is illustrated with three concrete application areas described in the abstract.

Limitations

• Row count and specific column definitions are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's small size (233.4 KB) suggests it contains methodological code and documentation rather than a large-scale data corpus.

Provenance

Source

figshare

Collection Method

Likely contains generated or simulated data to demonstrate the proposed computational framework.

Freshness

Last updated 2026-06-02 21:18:17; freshness should be verified.