{"categories":[{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"NeuralFoil predicts airfoil aerodynamic coefficients (lift, drag, moment) and boundary layer properties using physics-informed neural networks trained on millions of XFoil simulations, running in pure Python+NumPy.","skillfed_tags":["aerodynamics","machine-learning","optimization"],"use_cases":["Rapid aerodynamic polars for aircraft conceptual design when XFoil convergence is unreliable or too slow","Gradient-based airfoil shape optimization using the smooth, continuous predictions and confidence flagging","Batch analysis of hundreds of airfoils at multiple Reynolds numbers and angles of attack","Aerodynamic analysis of out-of-distribution or novel airfoil geometries where training data is sparse","Real-time aerodynamic lookup in flight simulation or control law design needing fast, guaranteed convergence"],"what_it_does":"NeuralFoil is a fast aerodynamic analysis tool that replaces or complements XFoil for predicting airfoil performance. It uses neural networks trained on nearly 8 million XFoil runs, embedded with physics-based invariants, to estimate lift, drag, moment coefficients, and boundary layer properties across a wide range of angles of attack and Reynolds numbers. The package runs entirely in NumPy (trained in PyTorch but executed without PyTorch at runtime) and comes with eight model sizes trading accuracy for speed.\n\nThe tool is designed for aircraft design and optimization workflows where speed and reliability matter more than XFoil's occasional convergence failures. It returns an `analysis_confidence` metric flagging uncertain or out-of-distribution queries, making it useful for robust design optimization. The core package is lightweight (under 500 lines of user-facing code) and accepts airfoil input as .dat files, coordinate arrays, or aerosandbox airfoil objects.","worth_installing":"Yes. NeuralFoil is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and requires only two lightweight dependencies. It solves a real problem (XFoil speed and convergence) for aerodynamics-focused Python workflows. The tradeoff is accuracy within a few percent of XFoil\u2014acceptable for design and optimization. Install if you do airfoil analysis; skip if you need XFoil's exact predictions or work outside aerodynamics."},"id":"neuralfoil","links":{"html":"https://skillfed.io/packages/neuralfoil","md":"https://skillfed.io/packages/neuralfoil.md","pypi":"https://pypi.org/project/neuralfoil/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-11","license_spdx":null,"license_treatment":"permissive","name":"NeuralFoil","python_support":"supports_current","summary":"NeuralFoil is an airfoil aerodynamics analysis tool using physics-informed machine learning, in pure Python/NumPy."},"popularity":{"monthly_downloads":142880,"position":11198,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.3"}