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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.
NeuralFoil is a practical airfoil aerodynamics analysis tool using physics-informed machine learning, exposed to end-users in pure Python/NumPy.
| Date | Stars |
|---|---|
| 2026-07-31 | 453 |
| 2026-08-05 | 455 |
| 2026-08-06 | 455 |
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<p align="center">
<img src="./media/neuralfoil_logo.svg" width="800" />
</p>
by [Peter Sharpe](https://peterdsharpe.github.io) (<pds [at] mit [dot] edu>)
[](https://pepy.tech/project/neuralfoil)
[](https://pepy.tech/project/neuralfoil)
[](https://github.com/peterdsharpe/NeuralFoil/actions/workflows/run-pytest.yml)
[](https://pypi.org/project/NeuralFoil/)
[](https://opensource.org/licenses/MIT)
[](https://arxiv.org/abs/2503.16323)
-----
**NeuralFoil** is a tool for rapid aerodynamics analysis of airfoils, similar to [XFoil](https://web.mit.edu/drela/Public/web/xfoil/). NeuralFoil is [a hybrid of physics-informed machine learning techniques and analytical models, leveraging domain knowledge](./paper/out/main.pdf). Its learned core is trained on [nearly 8 million XFoil runs](#geometry-parameterization-and-training-data).
NeuralFoil is available here as a pure Python+NumPy standalone (trained in PyTorch, runtime-executed in NumPy), but it is also [available within AeroSandbox](#extended-features-transonics-post-stall-control-surface-deflections), which extends it with advanced features. With this extension, NeuralFoil can give you **viscous, compressible airfoil aerodynamics for (nearly) any airfoil, with control surface deflections, across $360^\circ$ angle of attack, at any Reynolds number, all very quickly** (~5 milliseconds). And, it's guaranteed to return an answer (no non-convergence issues), it's vectorized, and it's $C^\infty$-continuous (critical for gradient-based optimization). For aerodynamics experts: NeuralFoil will also give you fine-grained boundary layer control ($N_{\rm crit}$, forced trips) and information ($\theta$, $H$, $u_e/V_\infty$, and pressure distributions).
A unique feature is that NeuralFoil also assesses its own trustworthiness, yielding an [`"analysis_confidence"`](#accuracy) output: queries where flow is sensitive or strongly out-of-distribution are flagged. This is especially useful for design optimization, where [constraining this uncertainty metric](https://github.com/peterdsharpe/AeroSandbox/blob/master/tutorial/06%20-%20Aerodynamics/02%20-%20AeroSandbox%202D%20Aerodynamics%20Tools/02%20-%20NeuralFoil%20Optimization.ipynb) ensures designs are [robust to small changes in shape and flow conditions.](https://web.mit.edu/drela/OldFiles/Public/papers/Pros_Cons_Airfoil_Optimization.pdf)
NeuralFoil is [~30x faster than XFoil for a single analysis, and ~1000x faster for multipoint analysis](#speed), all with [minimal loss in accuracy compared to XFoil](#accuracy). Due to the diversity of training data and the embedding of several physics-based invariants, [this accuracy is seen even on out-of-distribution airfoils](#accuracy) (i.e., airfoils it wasn't trained on). More comparisons to XFoil are [here](#xfoil-benefit-question). NeuralFoil aims to be lightweight, with [minimal dependencies](#dependencies-question) and a [small and easily-understood code-base](neuralfoil/main.py) (<500 lines of user-facing code).
```
pip install neuralfoil
```
(The above figure is an excerpt from the [author's PhD thesis](#citing-neuralfoil))
**[For example usage of NeuralFoil, see here](https://github.com/peterdsharpe/AeroSandbox/tree/master/tutorial/06%20-%20Aerodynamics/02%20-%20AeroSandbox%202D%20Aerodynamics%20Tools).**
**[For more technical details, validation cases, and case studies, see the pre-print of the NeuralFoil paper](./paper/out/main.pdf).** ([Citation info here](#citing-neuralfoil)).
## Overview
NeuralFoil comes with 8 different neural network models, with increaExcerpt of 31,796 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:30b19d97cd4449c4, llm:description, topics: 'physics-informed machine learning', 'aerodynamic-analysis', 'airfoil', 'cfd', trained on XFoil, provides fast aerodynamics predictions in NumPy
matched fp:30b19d97cd4449c4, llm:description, topics: 'physics-informed machine learning', 'aerodynamic-analysis', 'airfoil', 'cfd', trained on XFoil, provides fast aerodynamics predictions in NumPy