(IN BRIEF) An international research team at GSI/FAIR has developed a new artificial intelligence-based simulation model called RHINE to improve understanding of how heavy elements form during stellar events such as neutron star mergers. The model uses machine learning and … Read the full press release →
Posted in Artificial Intelligence (AI), Education, Energy, Gas & Oil, European Union, Germany, Government, Science, Technology
Tagged artificial intelligence, Artificial Intelligence Helps Researchers Study How Neutron Star Mergers Produce Heavy Elements, astrophysical simulations, computational physics, deep learning, Dr Oliver Just, Dr Zewei Xiong, electromagnetic radiation, element formation, ERC, European Research Council, FAIR facility, GSI/FAIR, GSI/FAIR Advances Nuclear Astrophysics Research with Publicly Available RHINE Simulation Code, GSI/FAIR Research Team Develops AI-Based Simulation Model to Advance Understanding of Neutron Star Mergers, heavy element formation, heavy nuclides, hydrodynamic simulations, kilonova, Machine Learning, neural network, neutron star mergers, New RHINE Model Uses Machine Learning to Improve Simulations of Heavy Element Formation in Stellar Events, nuclear astrophysics, Nuclear Astrophysics & Structure, nuclear reactions, Physical Review D, r-process heating, r-process nucleosynthesis, rapid neutron capture, reference calculations, RHINE, source code, stellar events, stellar explosions
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