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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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.
| Date | Stars |
|---|---|
| 2026-07-24 | 1272 |
| 2026-07-25 | 1274 |
| 2026-07-28 | 1286 |
| 2026-07-30 | 1288 |
| 2026-08-06 | 1288 |
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# [AeroSandbox](https://peterdsharpe.github.io/AeroSandbox/) :airplane:
by [Peter Sharpe](https://peterdsharpe.github.io) (<pds [at] mit [dot] edu>)
[](https://pepy.tech/project/aerosandbox)
[](https://pepy.tech/project/aerosandbox)
[](https://github.com/peterdsharpe/AeroSandbox/actions/workflows/run-pytest.yml)
[](https://pypi.python.org/pypi/aerosandbox)
[](https://aerosandbox.readthedocs.io/en/master/?badge=master)
[](https://opensource.org/licenses/MIT)
**AeroSandbox is a Python package that helps you design and optimize aircraft and other engineered systems.**
At its heart, AeroSandbox is an optimization suite that combines the ease-of-use of [familiar NumPy syntax](aerosandbox/numpy) with the power of [modern automatic differentiation](./tutorial/10%20-%20Miscellaneous/03%20-%20Resources%20on%20Automatic%20Differentiation.md).
This automatic differentiation dramatically improves optimization performance on large problems: **design problems with tens of thousands of decision variables solve in seconds on a laptop**. AeroSandbox also comes with dozens of end-to-end-differentiable aerospace physics models, allowing you to **simultaneously optimize an aircraft's aerodynamics, structures, propulsion, mission trajectory, stability, and more.**
Keeping AeroSandbox easy to learn and use is a top priority. *Complexity is optional* - you can use AeroSandbox's built-in physics models where helpful, or you can drop in arbitrary custom physics models of your own.
```
pip install aerosandbox[full]
```
-----
### What can I do with AeroSandbox?
Use AeroSandbox to design and optimize entire aircraft:
<table>
<tr>
<td width="50%" valign="top">
<p align="center">
<a href="https://github.com/peterdsharpe/Feather-RC-Glider"><i>Feather</i> (an ultra-lightweight 1-meter-class RC motor glider)</a>
</p>
<img src="https://raw.githubusercontent.com/peterdsharpe/Feather-RC-Glider/master/CAD/feather.png" alt="Feather first page">
</td>
<td width="50%" valign="top">
<p align="center">
<a href="https://github.com/peterdsharpe/solar-seaplane-preliminary-sizing"><i>SEAWAY-Mini</i> (a solar-electric, 13' wingspan seaplane)</a>
</p>
<img src="https://raw.githubusercontent.com/peterdsharpe/solar-seaplane-preliminary-sizing/main/CAD/renders/seaway_mini_packet_Page_1.png" alt="Seaway-Mini first page">
</td>
</tr>
</table>
Use AeroSandbox to support real-world aircraft development programs, all the way from your very first sketch to your first-flight and beyond:
<table>
<tr>
<td width="50%" valign="top">
<p align="center">
<a href="https://github.com/peterdsharpe/DawnDesignTool">Initial concept sketches + sizing of <i>Dawn</i> (a solar-electric airplane for climate science research) in AeroSandbox, Spring 2020</a>
</p>
<img src="./media/images/dawn1-first-sketch.png" alt="Dawn initial design">
</td>
<td width="50%" valign="top">
<p align="center">
<a href="https://youtu.be/CyTzx9UCvyo"><i>Dawn</i> (later renamed <i>SACOS</i>) in first flight, Fall 2022</a>
</p>
<p align="center"><a href="https://www.electra.aero/news/sacos-first-flight">(A massive build effort with excellent engineering and coordination by Electra.aero!)</a></p>
<img src="./media/images/SACOS%20First%20Flight%20Zoomed.jpg" alt="SACOS firsExcerpt of 16,587 characters
Read on GitHub
Peter Sharpe · @NVIDIA · United States
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Bane Sullivan · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a462803b78861674, topic:vlm
matched fp:a462803b78861674, topic:simulation