GSI/FAIR develops RHINE simulation

An international research team at GSI/FAIR has developed a novel simulation model named RHINE, which utilizes deep learning and neural networks to understand element formation and energy release during r-process nucleosynthesis in stellar events like neutron star mergers. This model efficiently approximates r-process heating, which was previously computationally prohibitive, allowing for more detailed hydrodynamic simulations. Dr. Oliver Just and Dr. Xu Zewei were key figures in this development. The RHINE source code is publicly available, and the project was co-funded by the Australia — Australian Research Council. The findings were published in Physical Review Letters, marking a significant advancement in astrophysics and AI integration.