10.18453/rosdok_id00004533 Schörner, Maximilian Tobias Maximilian Tobias Schörner http://d-nb.info/gnd/1317257995 https://orcid.org/0000-0001-7925-8917 Universität Rostock 2023 500 Natural sciences 530 Physics 2023 en https://purl.uni-rostock.de/rosdok/id00004533 urn:nbn:de:gbv:28-rosdok_id00004533-6 A profound understanding of warm dense matter properties is essential to unraveling the mysteries of planetary and stellar formation, evolution, and interior structure, as well as establishing inertial confinement fusion as a potential energy source. This thesis employs modern machine-learning approaches to leverage the capabilities of new laser and X-ray facilities. In the context of scattering experiments, the dynamic structure factor of the ions and electrons is employed to connect density functional theory simulations with scattering experiments on different energy scales.