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gpd-sensitivity-analysis

Compute dimensionless sensitivities S_i for GPD model inputs.

Updated Mar 15, 2026
Search Tags:#sensitivity-analysis#gpd#model-analysis#uncertainty-propagation#parameter-ranking#fusion-control
One-click install
npx skills add https://github.com/MichaelsEngineering/get-physics-done-test --skill gpd-sensitivity-analysis-michaelsengineering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpd-sensitivity-analysis
Source: https://github.com/MichaelsEngineering/get-physics-done-test/tree/main/.agents/skills/gpd-sensitivity-analysis
Command: npx skills add https://github.com/MichaelsEngineering/get-physics-done-test --skill gpd-sensitivity-analysis-michaelsengineering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps researchers determine which input parameters most influence Get Physics Done (GPD) outputs, enabling focused uncertainty reduction and more reliable model guidance.

Core Features & Use Cases

  • Catalogs input parameters by type (physical, numerical, approximation controls, and measured inputs) that affect a given output.
  • Computes dimensionless sensitivity coefficients S_i and absolute changes to quantify impact and propagate uncertainty.
  • Ranks parameters by contribution to output uncertainty and flags nonlinearities, divergences, and potential ill-conditioning.
  • Use case: optimize measurement and modeling efforts in fusion control problems, such as tokamak turbulence and confinement predictions.

Quick Start

Run a sensitivity analysis on the active GPD phase to identify top drivers of the target output.

Frequently Asked Questions about gpd-sensitivity-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify which input parameters drive my physics model outputs?▼

Sensitivity analysis identifies which inputs drive model outputs by computing dimensionless sensitivity coefficients S_i, quantifying their impact, and ranking parameters by their contribution to output uncertainty.

How does uncertainty propagation work for fusion control problems?▼

Uncertainty propagation in fusion control tracks nominal values and uncertainties across input parameters, computing absolute changes to quantify impact and flag nonlinearities or divergences that affect model reliability.

What is the best way to rank model parameters by their contribution to output uncertainty?▼

Ranking parameters by uncertainty contribution involves cataloging inputs by type, computing dimensionless sensitivities S_i, and sorting them to pinpoint which physical, numerical, or measured inputs most influence outputs.

Can I use sensitivity analysis for tokamak turbulence and confinement predictions?▼

Sensitivity analysis applies to fusion control problems like tokamak turbulence and confinement predictions, optimizing measurement and modeling efforts by identifying top drivers of target outputs.

Do I need partial derivatives to perform parameter ranking in GPD models?▼

Parameter ranking requires computing partial derivatives or finite-difference estimates to determine dimensionless sensitivities and quantify each input's contribution to output uncertainty.

What types of input parameters should I catalog for sensitivity analysis?▼

Sensitivity analysis catalogs input parameters by type, including physical, numerical, approximation controls, and measured inputs that affect a given output, ensuring comprehensive uncertainty quantification.

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