---
id: aerosandbox
version: "4.2.10"
license: MIT
license_treatment: permissive
maintenance: active
---

# AeroSandbox — AeroSandbox is a Python package that helps you design and optimize aircraft and other engineered systems.

License: permissive · Maintenance: active · Downloads: 161.1K/mo

## What it is and what it does

AeroSandbox is a Python optimization framework designed for aerospace engineering and general-purpose multidisciplinary design. At its core, it wraps automatic differentiation around NumPy-compatible syntax to solve large constrained optimization problems efficiently. The package ships with dozens of end-to-end-differentiable physics models covering aerodynamics, structures, propulsion, and trajectory simulation, allowing simultaneous optimization across multiple disciplines.

Typical workflows involve defining aircraft geometry, specifying design variables and constraints, then calling the optimizer to find the best configuration. You can also use AeroSandbox as a pure solver for nonlinear equations, boundary-value problems, or as a general optimization engine independent of aerospace. The package emphasizes ease of learning—built-in physics models are optional, and you can drop in arbitrary custom models. All inputs and outputs use SI units by default, with documented exceptions for angles (degrees for angle of attack and sideslip per aerospace convention).

Use it for:

- Design and optimize complete aircraft from concept to first-flight, balancing aerodynamics, weight, propulsion, and mission range.
- Perform aerodynamic shape optimization of wings and airfoils subject to structural and manufacturing constraints.
- Explore design tradeoffs early in conceptual design—e.g., how solar airplane size varies with latitude and season.
- Solve nonlinear boundary-value problems with automatic discretization and differentiation.
- Interface with external tools (AVL, XFLR5, XFoil, CAD software) to augment or validate models.
- Optimize unconventional propulsion systems, weights estimation, and electric motor matching for aircraft.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

AeroSandbox is an optimization toolkit for aircraft and engineered systems that combines NumPy syntax with automatic differentiation to solve large design problems, and includes end-to-end-differentiable aerospace physics models for aerodynamics, structures, propulsion, and trajectory optimization.

Yes. AeroSandbox is actively maintained, has no known vulnerabilities, and offers a rare combination of ease-of-use and power for aerospace design and general optimization. The low install friction, permissive MIT license, and broad applicability make it a strong choice for researchers, engineers, and students. Install the base package for core functionality, or add [full] for 3D visualization and CAD export if needed.

## Install

pip install aerosandbox
uv add aerosandbox
poetry add aerosandbox

## Installing AeroSandbox

Before you install: Low friction: pure Python wheel with ten runtime dependencies. Active maintenance—last release 40 days ago with 1301 GitHub stars and no known vulnerabilities. Requires Python 3.10+.

License in practice: MIT license (permissive): you can use, modify, and distribute freely in commercial and private projects with minimal restrictions, provided you include the original license notice.

Quickstart:

pip install aerosandbox

import aerosandbox as asb

# Define and optimize an aircraft
airplane = asb.Airplane()
# Then use optimization methods on the airplane object

Requires Python 3.10 or later. The full feature set (3D visualization, CAD export) requires optional dependencies; core optimization and physics models work with the base install.

Verify before relying:

- Whether casadi must be installed separately as a system dependency or installs automatically.
- Performance characteristics on problems with tens of thousands of decision variables on typical hardware.
- Whether automatic differentiation is compatible with all custom physics models users might provide.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 161.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

aircraft design optimization, aerodynamics simulation, automatic differentiation solver, aerospace engineering tools, wing and airfoil optimization, multidisciplinary design optimization, trajectory and mission planning, optimization, aerospace-engineering, automatic-differentiation

[View on SkillFed](https://skillfed.io/packages/aerosandbox) · [View on PyPI](https://pypi.org/project/aerosandbox/)