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peterdsharpe/AeroSandbox

Aircraft design optimization made fast through computational graph transformations (e.g., automatic differentiation). Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more. observed · 2026-08-28

github.com/peterdsharpe/AeroSandbox · homepage · Jupyter Notebook · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

73/100

  • Activity 91
  • Release rhythm 35
  • Longevity 100

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 2667
  • days_rel: n/a
  • days_push: 59
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

1315 stars · 205 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AeroSandbox is a Python library for designing and optimizing aircraft and other engineered systems using automatic differentiation. It provides differentiable physics models for aerodynamics, structures, propulsion, and trajectory analysis, feeding exact gradients to nonlinear optimizers like IPOPT.

Use cases

  • optimize an aircraft's aerodynamics, structures, and propulsion simultaneously
  • analyze airfoil performance with differentiable models like NeuralFoil
  • run vortex-lattice or lifting-line aerodynamic analysis on a wing
  • size a solar-electric aircraft or RC glider from scratch
  • solve large multidisciplinary design optimization problems with thousands of variables
  • compute stability derivatives for a whole aircraft
  • model propeller, electric motor, or turbofan performance in an optimization loop

When to choose

  • you need gradient-based optimization of coupled aerospace physics models
  • you want fast, differentiable aerodynamics analysis in pure Python
  • you're doing conceptual or preliminary aircraft design and want design closure in seconds
  • you prefer NumPy-style syntax without hand-deriving gradients

When to avoid

  • you need high-fidelity RANS CFD or detailed CAD-level structural FEA
  • you're not doing optimization and just need a standalone GUI-based analysis tool
  • you work outside Python or need certified/validated industrial aerospace tooling

Facets

library · maturity active

simulation math machine-learning simulation python cross-platform aircraft-design aerodynamics automatic-differentiation multidisciplinary-design-optimization vortex-lattice-method xfoil cfd propulsion trajectory-optimization mdao optimization aerospace-engineering algorithms engineering

3 sources

Member repositories

RepositoryRoleHealth v2
peterdsharpe/AeroSandboxmain73

For agents

markdown · JSON · MCP: product_card(name="peterdsharpe/AeroSandbox")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem