Update README.md

README.md

@@ -203,7 +203,7 @@ Why not just use XFoil directly?
 > - 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 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.
+>   - 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](https://dspace.mit.edu/handle/1721.1/119272) (intermittency), 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.
 > - While XFoil is ~1000x faster than RANS CFD, NeuralFoil [can be another ~1000x faster to evaluate than XFoil](#performance). NeuralFoil is also much easier to interface with on a memory level than XFoil, which means you won't find yourself I/O bound from file reading/writing like you will with XFoil.
 > - XFoil is not vectorized, which exacerbates the speed advantage of a (vectorized) neural network when analyzing large batches of airfoil cases simultaneously.