Once data are reconstructed, every voxel in SAXS tomography data contains a scattering pattern to be analyzed. This means a massive effort for data analysis (Step (E) in Fig. ) after reconstruction. Similar numbers of SAXS patterns need to be analyzed in other situations, for example when material structures are studied as function of physical parameters (temperature, pressure, pH, humidity, etc.) in multiple measurements. A recent review by Anker and coworkers addresses machine learning (ML) approaches to analyze a range of synchrotron-based experiment data, including SAXS but also powder diffraction, pair distribution function, inelastic neutron scattering and X-ray absorption spectroscopy data. While the traditional approach would be to fit a physical model to the data, supervised ML can be used to train a model for the prediction of structure based on data, but also to predict the scattering data based on a known structure and also to predict parameters based on some physical understanding of the system . In another recent work , a ML-based analysis of SAXS data is proposed, which is based on Gaussian random fields that avoids the common model fitting of the data.