Sensitivity Examples

These examples focus on practical interpretation of parameter sensitivities with the single-track model on the torch backend and the high-level apexsim.analysis.run_lap_sensitivity_study(...) API.

Study scope

Both example studies use local derivatives around one operating point and report an engineering-friendly %/% interpretation. The same four physical parameters are included:

1. Vehicle mass
2. Center of gravity height
3. Yaw inertia
4. Drag coefficient

Two objective metrics are evaluated:

1. Lap time \([s]\)
2. Energy consumption \([kWh]\)

Why this setup is useful

• It gives a consistent sensitivity baseline across different track classes.
• It highlights which parameters matter globally (lap time) vs. energetically.
• It uses one compact, model-agnostic API with clear parameter targets.
• The Spa notebook additionally compares quasi-static and transient sensitivities in an AD-first workflow to expose solver-path limitations (notably for yaw inertia).

Output artifacts

Each study exports:

• sensitivities_long.csv: one row per (objective, parameter) pair
• sensitivities_pivot.csv: compact parameter × objective sensitivity map
• sensitivity_bars.png: compact comparison plot for both objectives

All outputs are written below:

• examples/output/sensitivity/

Notebooks

1. Synthetic Single-Track Sensitivity
2. Spa Single-Track Sensitivity