Update README.md

README.md

@@ -16,7 +16,7 @@ by [Peter Sharpe](https://peterdsharpe.github.io) (<pds [at] mit [dot] edu>)
 
 NeuralFoil is available here as a pure Python+NumPy standalone, 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 practical: [robust to small changes in shape and flow conditions.](https://web.mit.edu/drela/OldFiles/Public/papers/Pros_Cons_Airfoil_Optimization.pdf)
+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 [~10x 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/gen2_5_architecture/main.py) (<500 lines of user-facing code).