gpd-sensitivity-analysis

Analyze physics model parameters by computing partial derivatives and condition numbers.

1|Updated Mar 29, 2026

Search Tags:#sensitivity analysis #model validation #parameter optimization #computational physics #physics modeling

Ready to run

npx skills add https://github.com/CharGrnmn/roomtemp-superconductor-gpd --skill gpd-sensitivity-analysis-chargrnmn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gpd-sensitivity-analysis
Source: https://github.com/CharGrnmn/roomtemp-superconductor-gpd/tree/main/.agents/skills/gpd-sensitivity-analysis
Command: npx skills add https://github.com/CharGrnmn/roomtemp-superconductor-gpd --skill gpd-sensitivity-analysis-chargrnmn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sympy, numpy, and includes scripts (resource) components.

What problem does it solve?

This Skill provides systematic sensitivity analysis to determine which input parameters most strongly affect output quantities in physics models, helping to prioritize efforts for parameter improvement.

Core Features & Use Cases

Sensitivity Analysis: Compute partial derivatives, condition numbers, and rank parameters by sensitivity.
Parameter Prioritization: Identify which measurements or calculations would most improve final results.
Use Case: For a complex physics model, use this Skill to determine which parameters contribute most to the output uncertainty, allowing for targeted efforts to reduce error.

Quick Start

Run the gpd-sensitivity-analysis skill with the target quantity 'energy' and parameters 'mass', 'coupling', and 'temperature'.

Frequently Asked Questions about gpd-sensitivity-analysis

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

FAQPage Schema

How do I perform sensitivity analysis on a computational physics model?▼

Sensitivity analysis identifies key parameters affecting output quantities in physics models by computing partial derivatives and condition numbers to rank parameters by their output sensitivity. This process requires Python with symbolic and numerical methods to prioritize parameter improvement efforts.

What is the best way to prioritize model parameters for optimization in computational physics?▼

Prioritizing model parameters for optimization involves calculating partial derivatives and condition numbers to rank input sensitivity. This systematic sensitivity analysis determines which parameters contribute most to output uncertainty, enabling targeted efforts to reduce error.

Can I use sympy and numpy for systematic sensitivity computation in physics modeling?▼

Yes, you can use sympy and numpy for systematic sensitivity computation in physics modeling. The Skill utilizes sympy for symbolic partial derivative computation and numpy for numerical methods to evaluate parameter sensitivity and validate the model.

Does sensitivity analysis help with model validation for complex physics calculations?▼

Sensitivity analysis helps with model validation by identifying which input parameters most strongly affect output quantities in complex physics calculations. By ranking parameter sensitivity, you can target specific measurements or calculations to most improve final results.

How do I rank parameters by sensitivity to reduce output uncertainty?▼

To rank parameters by sensitivity and reduce output uncertainty, compute partial derivatives and condition numbers for the target quantity against specified parameters. This identifies which measurements contribute most to uncertainty, allowing targeted error reduction.

When should I not use analytical methods for sensitivity analysis in physics models?▼

Analytical methods for sensitivity analysis may not be suitable when the physics model involves highly complex or non-linear equations that resist symbolic computation. In such cases, numerical methods using numpy can be applied to approximate parameter sensitivity instead.