Understanding neutron star mergers with artificial intelligence

An international research team at GSI/FAIR used a novel simulation model based on machine learning to gain a better understanding of element formation during neutron star mergers.
The research involved deep learning with a neural network to model the energy release during r-process nucleosynthesis in hydrodynamic simulations for the first time.
The new model, RHINE, uses artificial intelligence to describe energy release during the r-process, significantly impacting the ejected material’s dynamics and velocity in stellar events.
The RHINE model is validated as an efficient way to reduce computing time and could link experimental data from the FAIR facility with observations of stellar explosions.
The RHINE source code is made publicly available and was co-funded by the European Research Council.
Published results form an article in Physical Review D, with a focus on computational modeling for element formation in stars.

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