We investigate the large deviations principle (which concerns sequences of exponentially small sets) for the isoperimetric problem on product Riemannian manifolds $M^{n}$ equipped with product probability measures $ν^{\otimes n}$, where $M$ is a Riemannian manifold satisfying curvature-dimension bound $\mathrm{CD}(0,\infty)$. When the probability measure $ν$ admits a finite moment generating function for squared distance, we establish an exact characterization of the large deviations asymptotics for the isoperimetric profile, which shows a precise equivalence between these asymptotic isoperimetric inequalities and nonlinear log-Sobolev inequalities. It is observed that the product of two relative entropy typical sets (or empirically typical sets) forms an asymptotically optimal solution to the isoperimetric problem. The proofs in this paper integrate tools from information theory, optimal transport, and geometric measure theory.

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