Update README.md

README.md

@@ -201,7 +201,7 @@ Why not just use XFoil directly?
 > 
 > - XFoil exhibits hysteresis: you can get slightly different solutions (for the same airfoil, $\alpha$, and $Re$) depending on whether you sweep $\alpha$ up or down, as Newton iteration is resumed from the last converged solution and uniqueness is not guaranteed. This hysteresis can be a big problem for design optimization.
 > - XFoil is not differentiable, in the sense that it doesn't tell you how performance changes w.r.t. airfoil shape (via, for example, an adjoint). That's okay—NeuralFoil doesn't either, at least out-of-the-box. However, the "path to obtain an efficient gradient" is very straightforward for NeuralFoil's pure NumPy code, where many excellent options exist (e.g., JAX). In contrast, gradient options for Fortran code (the language XFoil is in) either don't exist or are significantly less advanced (e.g., Tapenade). The most promising option for XFoil is probably [CMPLXFOIL](https://github.com/mdolab/CMPLXFOIL), which computes complex-step (effectively, forward-mode) gradients. However, even if you can get a gradient from XFoil, it still may not be very useful, because...
-> - XFoil's solutions lack $C^1$-continuity. NeuralFoil, by contrast, is guaranteed to be $C^\infty$-continuous by construction. This is critical for gradient-based optimization.
+> - XFoil's solutions intrinsically lack $C^1$-continuity. NeuralFoil, by contrast, is guaranteed to be $C^\infty$-continuous by construction. This is critical for gradient-based optimization.
 >   - Even if one tries to compute gradients of XFoil's outputs by finite-differencing or complex-stepping, these gradients are often inaccurate.
 >   - A bit into the weeds, but: this comes down to how XFoil handles transition (onset of turbulence). XFoil does a cut-cell approach on the transitioning interval, and while this specific cut-cell implementation restores $C^0$-continuity (i.e., transition won't truly "jump" from one node to another discretely), gradients of the laminar and turbulent BL closure functions still change at the cell interface due to the differing BL parameters ($H$ and $Re_\theta$) from node to node. This loses $C^1$ continuity, causing a "ragged" polar at the microscopic level. In theory $C^1$-continuity could be restored by also blending the BL shape variables through the transitioning cell interval, but that unleashes some ugly integrals and is not done in XFoil.
 >     - For more on this, see [Adler, Gray, and Martins, "To CFD or not to CFD?..."](http://websites.umich.edu/~mdolaboratory/pdf/Adler2022c.pdf), Figure 7.