Update README.md

Author: heptaflar

README.md

@@ -12,8 +12,8 @@ algorithms, implementations, and dependencies evolve. Algorithmic
 differentiation (AD) provides a better foundation because it derives local
 linearizations directly from the implementation whenever needed, so sensitivity
 information automatically stays consistent with the code. Combined with random
-sampling methods for strongly nonlinear behaviour, this defines uncertainty
-propagation in terms of algorithmically differentiable programs. AD frameworks
+sampling methods for strongly nonlinear behaviour, uncertainty propagation
+is defined in terms of algorithmically differentiable programs. AD frameworks
 treat inputs, outputs, sensitivities, and uncertainties as dynamic tensor‑valued
 objects rather than forcing the uncertainty calculus into a fixed set of
 closed‑form formulas.