Update README.md

README.md

@@ -136,7 +136,7 @@ NeuralFoil can be used for airfoil shape optimization, in conjunction with [Aero
 
 ![daedalus_optimization.svg](./paper/TeX/figures/daedalus_optimization.svg)
 
-Here, NeuralFoil achieves performance equal to expert-designed airfoils. The entire optimization process takes roughly 30 seconds on a PC; optimization studies with a lower NeuralFoil `model_size` value can run as quick as half a second. Notably, if the problem formulation is well-posed, NeuralFoil will not "over-optimize" to achieve a solution that performs well at on-design conditions but very poorly when off-design. Compared to optimization by simple [wrapping of XFoil](https://github.com/montagdude/Xoptfoil) with a gradient-based optimizer, the resulting airfoils achieve better aerodynamic performance due to the [ragged nature of XFoil's gradients](https://websites.umich.edu/~mdolaboratory/pdf/Adler2022c.pdf). (Also, NeuralFoil-based optimization is much faster.)
+Here, NeuralFoil achieves performance equal to expert-designed airfoils. The entire optimization process takes roughly 30 seconds on a PC; optimization studies with a lower NeuralFoil `model_size` value can run as quick as half a second. Notably, if the problem formulation is well-posed, NeuralFoil will not "over-optimize" to achieve a solution that performs well at on-design conditions but very poorly when off-design. Compared to optimization by simple wrapping of XFoil with a gradient-based optimizer, the resulting airfoils achieve better aerodynamic performance due to the [ragged nature of XFoil's gradients](https://websites.umich.edu/~mdolaboratory/pdf/Adler2022c.pdf). And, compared to [wrapping XFoil with a gradient-free optimizer](https://github.com/jxjo/Xoptfoil2), NeuralFoil-based optimization is much faster.
 
 ## Extended Features (transonics, post-stall, control surface deflections)