  Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imaging
technique used in material research to study nanoscale materials.
Reconstruction of the parameters of an imaged object imposes an ill-posed
inverse problem that is further complicated when only an in-plane GISAXS signal
is available. Traditionally used inference algorithms such as Approximate
Bayesian Computation (ABC) rely on computationally expensive scattering
simulation software, rendering analysis highly time-consuming. We propose a
simulation-based framework that combines variational auto-encoders and
normalizing flows to estimate the posterior distribution of object parameters
given its GISAXS data. We apply the inference pipeline to experimental data and
demonstrate that our method reduces the inference cost by orders of magnitude
while producing consistent results with ABC.
