Update README.md

README.md

@@ -205,7 +205,7 @@ Why not just use XFoil directly?
 >   - 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](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.
+> - 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. ([Memory interfacing with XFoil is possible](https://github.com/DARcorporation/xfoil-python), but rare.)
 > - XFoil is not vectorized, which exacerbates the speed advantage of a (vectorized) neural network when analyzing large batches of airfoil cases simultaneously.
 > - XFoil is not guaranteed to produce a solution. Instead, XFoil often crashes when "ambitious" calculations are attempted, rather than producing a less-accurate answer. In some applications, that's okay or even desirable; in others, that's a deal-breaker. Example applications where this is a problem include:
 >   - Real-time control, where one wants to estimate forces (e.g., for a MPC trajectory), but you can't have the controller crash if XFoil fails to converge or hangs the CPU.