arXiv:2611.09574v1 Announce Type: new Abstract: We study a Bayesian inverse problem in EUV reflectometry, where optical constants and layer thicknesses are inferred from angle- and wavelength-resolved reflection measurements. The physical process is modeled and evaluated using an efficient forward model based on the transfer matrix method. As an alternative to computationally expensive sampling-based inference, we employ an Eulerian, entropically regularized Jordan-Kinderlehrer-Otto (JKO) scheme that minimizes the Kullback--Leibler divergence via proximal steps in the Wasserstein space. Using the Benamou--Brenier dynamic formulation and a Hopf-Cole transform, each JKO step is reformulated as a pair of heat equations coupled only through their initial and terminal conditions, enabling an efficient fixed-point solver. The resulting high-dimensional densities are discretized on tensor-product grids and represented in tensor-train format, exploiting Kronecker structure for scalable computation. Preconditioning based on estimated posterior moments further improves tractability. We test the method with numerical experiments on EUV reflectometry data for a ruthenium--silicon ($\ce{Ru}$ on $\ce{Si}$) specimen and compare the learned tensor-train posterior to ensemble Markov chain Monte Carlo (MCMC) reference samples. We demonstrate a use case in which we are able to reuse the tensor-train posterior to obtain samples for shifted target distributions with less computational effort than a comparable MCMC method.